# Creating awareness about security and safety on highways to mitigate
wildlife-vehicle collisions by detecting and recognizing wildlife fences using
deep learning and drone technology

Irene Nandutu  Marcellin Atemkeng  Patrice Okouma  Nokubonga Mgqatsa  Jean
Louis Ebongue Kedieng Fendji  Franklin Tchakounte  Department of Mathematics,
Rhodes University, 6139 Makhanda, South Africa  Department of Zoology and
Entomology, Rhodes University, 6139 Makhanda, South Africa  Department of
Mathematics and Computer Science, Faculty of Science, University of
Ngaoundéré, Cameroon  Department of SFTI, School of Chemical Engineering and
Mineral Industries, University of Ngaoundéré, Cameroon  Department of Computer
Engineering, University Institute of Technology, University of Ngaoundéré,
Ngaoundéré P.O. Box 454, Cameroon

###### Abstract

In South Africa, it is a common practice for people to leave their vehicles
beside the road when traveling long distances for a short comfort break. This
practice might increase human encounters with wildlife, threatening their
security and safety. Here we intend to create awareness about wildlife
fencing, using drone technology and computer vision algorithms to recognize
and detect wildlife fences and associated features. We collected data at
Amakhala and Lalibela private game reserves in the Eastern Cape, South Africa.
We used wildlife electric fence data containing single and double fences for
the classification task. Additionally, we used aerial and still annotated
images extracted from the drone and still cameras for the segmentation and
detection tasks. The model training results from the drone camera outperformed
those from the still camera. Generally, poor model performance is attributed
to (1) over-decompression of images and (2) the ability of drone cameras to
capture more details on images for the machine learning model to learn as
compared to still cameras that capture only the front view of the wildlife
fence. We argue that our model can be deployed on client-edge devices to
inform people about the presence and significance of wildlife fencing, which
minimizes human encounters with wildlife, thereby mitigating wildlife-vehicle
collisions.

###### keywords:

, detection , segmentation , roadkill , wildlife vehicle collisions , wildlife
fencing , tourists safety , tourists security

††journal: Journal of LaTeX Templates

##  1 Introduction

Wildlife fencing systems are popular in Africa and used extensively to
minimize wildlife-vehicle collision (WVC) cases [1]. The fences are
advantageous since they reduce: (1) threats to wildlife from direct human
activities (2) conflict between people and wildlife, and (3) zoonotic diseases
[2]. Fences can also bring about challenges, particularly when they are (1)
too low, enabling wildlife to crawl under easily, (2) or have loose wires
and/or wires spaced too closely together, and (3) are difficult for fleeing
animals or birds to see, creating a barrier to wildlife [3]. Barriers of
fences to wildlife are (1) limiting foraging opportunities, (2) interfering
with connectivity, and (3) failure to easily find new social groups [1]. This
study captures wildlife fences from South Africa’s national routes (N2). The
implemented fences are either double or single wildlife fences, and the
reserves are fenced following South African laws and regulations [4]. The
implemented wildlife fences are: (1) single and double fences without
electricity, meaning no dangerous animals in the property (i.e. game reserve),
(2) the double fences ensure high double protection in case of failure of the
inner fence to protect wildlife from escaping. The fence owners have some time
to rescue the animal before it runs through the highway fence, and (3) the
single and double fences can also be electric, meaning that dangerous animals
such as lions (Panthera leo) and leopards (Panthera pardus) are in the
property, and road users’ expectations are to take extra precautions while
using roads. Taking extra precautions when traveling on national roads might
lead to a reduction of WVC cases and human deaths. The deaths on highways are
worrying in Sub-Sahara Africa [1, 5] and South Africa [6] because of the high
numbers of mortality rates of wildlife on highways [7]. In addition, the
deaths soar in South Africa due to tourists’ inability to detect wildlife in
the neighborhood. This prompts tourists to come out of their vehicles for a
short comfort break, making them vulnerable to attacks from dangerous animals.
Generally, the high numbers of WVC continue escalating with extreme cases
recorded in both developed [8, 9, 10] and underdeveloped [6, 11, 5] countries.
These elevated collisions have damaged properties, wildlife, and human lives
and are very costly to the economy [12]. Identifying ways to minimize these
roadkill cases is paramount. Lately, authors are solving this issue by using
modeling and prediction [7] and traditional systems such as overpasses,
underpasses, and fencing [13]. On the other hand, intelligent systems using
sensors have also been deployed on highways in developed countries to minimize
wildlife mortality rates [14]. The intelligent systems are; break-the-beam
[15], area cover [16], and buried cable [17] wildlife detection systems. The
intelligent systems detect the presence of wildlife and alert the driver about
wildlife on the road before a driver arrives to or near the WVC area.

Detecting WVC incidences is well attempted in the Northern hemisphere using
intelligence systems. In other studies, WVC location detection using
classification and clustering algorithms has failed to show acceptable results
[18]. These challenges are solved using time series analysis linear models
such as Autoregressive Integrated Moving Average (ARIMA) and autoregressive
moving average, which have outperformed state-of-the-art models [19]. So far,
work on WVC prediction and time series analysis are ongoing [20, 21], and
witnessed in Africa and South Africa [7, 22]. However, findings show that
these ARIMA models have some shortcomings that need to be addressed, and a
research gap still exists in this area for future researchers. Some of these
shortcomings are the failure to detect a false absence (or presence) of
roadkill location and spatial bias in road mortality patterns. Due to
weaknesses in previous techniques above, especially those applicable in South
Africa [14] in detecting roadkill areas, we propose and use a novel technique
that alerts drivers of the presence of wildlife using a traditional fencing
system and computer vision techniques to detect wildlife fences and wildlife
fence features. This work is two-fold; (1) we classify and recognize if a
wildlife fence is a single or double fence, (2) to minimize noise, we segment
wildlife fence insulators as an approach that detects electric wildlife
fences.

In this study, we associate areas with wildlife fences and wildlife fence
insulators as unsafe for people to get off their vehicles, and drivers should
proceed cautiously. Avoiding human encounters with wildlife and WVC incidents
helps alert residents and tourists about the safety and security of a
neighborhood, which contributes to improving visitors’ safety and minimizing
WVC cases. We want to eliminate WVC cases using a cost-effective yet reliable
alternative to intelligence systems. We do this by feeding the annotated
insulators and their corresponding masks to the U-Net model and fine-tune and
concatenate U-Net output prediction. The predictions are then fed into OpenCV
to detect insulators on wildlife fences. In addition, single and double fences
are input in a classification algorithm to recognize fences. The U-Net,
together with OpenCV has characteristics of low cost, high efficiency, and
large economic benefits [23]. U-Net is a popular segmentation algorithm used
in medical imaging [24]. This architecture is adopted in this work to identify
and detect areas where the wildlife fence electrocution and roadkill of
wildlife are popular. OpenCV is a cross-platform vision library used to
develop real-time computer vision applications. It focuses on image
processing, video capture, and analysis, including face and object detection
[25]. In addition, classification techniques applied to wildlife fencing are
convolution neural networks (CNN) and ResNet. The data collected for this
study comprises two datasets, namely, aerial and still images. In this paper,
we assessed the prospect of using still and aerial fence images to improve the
safety and security of tourists and residents. Our specific objectives were to
1) determine whether wildlife fence and electric fence insulators extracted
from still and aerial images could be used as indicators to alert the presence
of WVC areas, 2) determine whether the image and associated concatenated mask
fed as input to a machine learning algorithm can detect electric fence areas
with and without insulators, 3) determine whether the wildlife fence images
fed in a classification algorithm can recognize single and double fences and
lastly, 4) determine whether OpenCV would detect the electric fence regions
with insulators segmented by the machine learning algorithm. Given that
wildlife fences and also insulators are unique features on South African
highways, we predicted that they would serve as a reliable indicator of
informing tourists and citizens about the presence of an animal in the
vicinity by sending alerts. This is significant because it detects wildlife
fences in real-time, contributing to mitigating WVC and improving tourists’
security and safety around game reserves. Additionally, each time a driver
detects WVC areas, the client should allow them to save the fence image and
global position system (GPS) coordinates. The dataset can further be
incorporated into maps or GPS to predict WVC areas and alert drivers on the
presence of wildlife on highways via GPS. Alternatively, this algorithm can be
deployed in self-driving cars. The images captured by drivers should be used
to retrain the model and improve performance via reinforcement learning.
Argumentatively, the proposed approach has not been applied before in
detecting wildlife fences to mitigate WVC.

The structure of the work is as follows; Section 2 discusses tourists’
security and safety issues as well as strategies to minimize the issues,
existing techniques for detecting WVC area and proposes a new technique to
detect WVC areas, and finally, wildlife-vehicle collision prevention. Section
3 discusses the structure of a wildlife fence and the associated components.
Section 4 discusses the study’s materials and methods. Section 5 discusses the
deep learning models used and optimization. Section 6 discusses the evaluation
metrics. Section 7 provides the model experiments and results, and Section 7.5
discusses the significance of the results, while Section 8 proposes future
works and concludes the study.

##  2 Relevance of the work

###  2.1 Tourists security and safety

Tourism is an extremely competitive business, with a core objective of
attracting tourists and consequently influencing their tourism destinations. A
tourism destination is a particular geographic region within which the visitor
enjoys various types of travel experiences [26]. In South Africa, the
competitive advantage of the tourist sector is popularly backed by science,
technology, information, and innovation. The conservationists in this country
focus on managing natural resources by creating innovative solutions which
contribute to fostering tourist destinations. In this regard, South Africa is
a blessed country with a well-connected network of 6.3% national parks and
private nature reserves [27]. The latter trend greatly because it meets the
demand of the environmentally sensitive visitor. Even though a conducive
environment is implemented for tourists in most of these parks, several
factors still limit the effectiveness of the tourism industry in South Africa,
and these are; (1) fewer funds allocated to the tourism sector, (2) limited
integration of local communities and previously neglected groups into tourism,
(3) inadequate tourism education, training, and awareness, and (4) tourism
security and safety. These challenges discourage potential tourists from
visiting South Africa, negatively reducing tourism demand and leading to low
tourist destinations. To minimize the challenges, the Western cape government
of South Africa implemented a six-leveler strategy for tourism safety, and
these are; (1) a tourism safety forum, which forms a formally structured,
multi-stakeholder, and collaborative safety forum, (2) establishing a multi-
stakeholder enforcement task team, (3) provision of tourists safety response
by providing assistance and supporting tourists while in distress, (4) to
identify and develop tourists safety and security technology platform that can
act as an enabler to help address and solve core tourists safety risks, (5) to
develop a tourism safety comprehensive communication strategy that integrates
all important stakeholders, (6) tourism safety interventions and partnerships
by identifying synergies that enable funding of cross-platform initiatives and
skills developments. Our work implements strategy four of the Western cape
government, one of the provinces of South Africa, which proposes to innovate a
technology platform that helps address tourists’ safety and security risks
[28]. It is worth noting that tourism security is a major constraint to
overseas tourism growth. Safety and security considerations are important
aspects of increasing tourism destinations in a country. The success or
failure of a tourism destination depends on the possibility of providing a
safe and secure environment for visitors [29]. This study seeks to improve the
safety and security of tourists on highways by intervening using artificial
intelligence algorithms to minimize wildlife encounters with tourists.
Tourists on South African highways need to be made aware of the presence of
wildlife fences because some visitors tend to urinate or relax around these
demarcated wildlife places enjoying the scenery with no idea that some places
have dangerous wildlife. Argumentatively, the machine learning algorithms
developed in this study can be incorporated into maps and deployed on car
dashboards or driverless cars to automatically detect a wildlife fence and
record and save the GPS coordinates and other parameters. The purpose of this
work is to inform tourists of the presence of wildlife and wildlife fencing,
providing security by minimizing tourists wildlife encounters between wildlife
and tourists, which finally leads to the mitigation of WVC.

###  2.2 Wildlife roadkill areas detection

Roadkill areas vary depending on the season, spatial and temporal variations,
duration of monitoring, and taxa [30].in Researchers in developed economies
use intelligent animal detection systems (ADS) [15, 31, 32, 33, 34, 35, 36,
16, 15, 31, 37, 38, 17, 39, 40, 41, 42, 43, 44] to detect roadkill areas.
These systems are usually costly, limiting their implementation in Africa
since the African governments are poor. Popularly, in time series analysis,
the roadkill areas are detected using latitude and longitude coordinates
collected by researchers and highway maintenance personnel. However, these
detected roadkill areas tend not to align well with sites of most serious
animal crossing attempts due to imperfect detections [45]. To address the
weakness of imperfect detections and the failure of African governments and
organizations to implement a cheaper and novel approach for mitigating WVC. We
propose and design an artificial intelligence mitigating strategy using
classification and segmentation techniques to detect wildlife fences and their
associated features called insulators in WVC locations. The algorithms can
further be deployed on client-edge devices to assist drivers in detecting
fences when driving the car. The camera can see the wildlife fence image and
save the GPS coordinates. Further, such a dataset can be integrated into maps
and GPS satellites to predict and visualize future WVC locations to drivers’
devices far away before they arrive at a WVC location. Such solutions should
be ethically aligned to ensure no harm is caused to animals and humans [46].

###  2.3 Wildlife vehicle collision prevention

The WVC is a global problem that has escalated in developed and developing
countries. United States registers 1,500,000 WVC cases, and Canada experiences
the death of 4 to 8 animals per day [8]. In South Africa in 2015, the greater
Mapungubwe transfrontier conservation area record shows 991 and 36 WVC on
paved and unpaved roads, respectively [6]. To prevent WVC, mitigation measures
are implemented. These are overpasses, underpasses, wildlife fencing, break-
the-beam [15, 31, 32, 33, 34], area cover [35, 36, 16, 15, 31, 37, 38], buried
cable [17, 39, 40], driver assistance [41], autonomous vehicles [42], and
mobile mapping ADSs [43, 44]. Intelligent systems are popular in developed
economies, with no attempts made in South Africa.

In this study, we propose and implement a novel mitigation measure that is
suitable for the South African economy. It is cheaper and uses existing
client-edge devices such as car dashboards and mobile phones. The measures use
wildlife fencing, drone technology, classification, and segmentation
algorithms to detect wildlife fences and the associated features. The
algorithm can be incorporated into maps to detect fences and alert tourists
and drivers about wildlife fences on highways. This approach improves the
security and safety of tourists and wildlife. In addition, it mitigates WVC.

##  3 Wildlife fencing: Single, Double, Electric

Wildlife mortality rates are prevalent in places without traditional
mitigation roadkill structures such as the wildlife fences [22]. Even in areas
with implemented wildlife fencing systems, the wildlife persistently jumps
over the fence and collides with cars leading to more cases of WVC [47].
Engineers have adopted the installation of electric fences to help mitigate
the alarming WVC cases. In designing electric fences, they use a stand-alone
fence or in conjunction with other fence types [48]. In most applications, the
fences contain energizers that use batteries recharged from solar panels or
wind turbines [48]. The fences also contain high-tensile electric wire fences
with wooden, metal, or plastic posts, as shown in Figure 1 (a). For an
electric wire to work effectively, it requires a set of components such as a
charging system, power source, regulator, energizer, earth system, conductor,
reels, posts and stakes, and insulators. The plastic standoffs (insulators)
are attached to wooden, metal, or plastic posts [48].

The charging system or power source is composed of the solar panel and wind
turbine as shown in Figure 1 (c) and Figure 1 (b), respectively. We note that
the wind turbine has a generator and an oscillator that converts the
mechanical energy generated by wind to electrical energy. Solar panels
generate power by converting sunlight into electricity. Another power source
is the battery charged by the solar panel or wind turbine. These two charging
systems (wind turbine and/or solar panel) produce the voltage and store it in
the battery. The stored energy is later used in case of limited energy. The
black cable from the wind turbine or solar panel carries or transmits the
voltage to the regulator.

The regulator, which is a white box on the left in Figure 1 (b) near the solar
panel and Figure 1 (c) near the wind turbine, controls the voltage needed to
kill the animals. For example, assume the wind turbine generates 50 volts or
10 volts. And if the system needs 24 volts, the regulator will reduce the
volts from 50 to 24 volts or increase the volts from 10 to 24 volts. The volts
from the regulator are transmitted to the energizer (three white boxes) shown
in Figures 1 (a), (b), and (c) through the black cable. The energizer has an
earthing system composed of blue and red cables, which allows the transfer of
electronics to and from the fence. The energizer achieves the former by
pushing an electric pulse through the red wire to the fence; the pulse travels
through the conductors and pressurizes the fence with excess electrons. That
pressure is measured in volts. When an animal touches the fence or wire,
excess electrons enter it and travel through the animal to the soil. After
exiting the animal, the pressurized electrons travel through the soil’s
moisture back to the energizer’s ground rod (blue cable). The electrons enter
the energizer via the ground rod (blue cable). The amount of electrons that
return is equal to the shock effect. An animal standing on the ground and
touching the electrified wire completes the circuit and receives intermittent
but regular shocks to deter it. The pulsed nature of the electricity enables
animals to move away from the fence, preventing electrocution.

Conductors or wires are equally an essential component of the fence as they
conduct electricity along the fence line. Some examples of a conductor are
steel wires. Also, reels significantly reduce the time it takes to dismantle a
fence. A reel post allows the fence wires to be independently tensioned and
keeps the wire fence taut. Permanent electric fence systems commonly use
wooden posts with insulators attached to them. Insulators prevent the
conductor or wire from touching the posts or stakes, which could lead to power
leakage [49]. A good insulator is vital in maintaining the fence’s
performance. In this study, we use wildlife fences and insulators, a feature
of a wildlife electric fence, to represent WVC areas where there is a
concentration of wildlife. We feed the annotated insulators and their
corresponding masks to the U-Net model, fine-tune and concatenate U-Net output
prediction. Next, the predictions are fed into OpenCV to detect insulators on
wildlife fences.

(a)

(b)

(c)

Figure 1: Structure of wildlife fence showing how electric fence works with
components such as in (a) as well as in the solar panel (b) and in wind
turbine (c)

##  4 Data collection and annotation

###  4.1 Study area

We took the images used in this study at Amakhala and Lalibela Private Game
Reserves in the Eastern Cape province of South Africa (Figure 2). The game
reserves are located on the highways on a flat and low terrain surrounded by
green vegetation. We captured images of the experimental site using a drone
and still cameras. To capture aerial images, a DJI Phantom 4 Pro UAV drone is
used and Nikon Z6 Mk 2 still camera helped in capturing still images. The
Nikon Z6 Mk 2 contains a 24.5MP FX-Format BSI CMOS Sensor, Actual: 25.28
Megapixel, Effective: 24.5 Megapixel $(6000\times 4000)$ ISO 100-51200, In-
body 5-axis Vibration Reduction Sensor incorporates a 273-point phase-
detection AF system, Mechanical Focal Plane Shutter, and Electronic Shutter,
and the Image File Format is RAW. The UAV has a 1” 20MP CMOS Sensor, Gimbal-
Stabilized 4K60 20MP Imaging, Ocusync Transmission, Flight Autonomy with
Redundant Sensors, five directions of obstacle sensing, and four directions of
obstacle avoidance, Maximum Flight Time of 30 Minutes, Hovering Accuracy
±0.98’ / 0.3 m Horizontal with Vision Positioning ±0.33’ / 0.1 m Vertical with
Vision Positioning and still Image Support – DNG. The highest flying height we
went with the drone was 204 meter.

Figure 2: The Amakhala and Lalibela game reserves, Eastern Cape Province,
South Africa, showing sampling points in green.

###  4.2 Data Acquisition

The data collection comprised three researchers, one professional
photographer, and one UAV pilot. The photographers captured scenes of wildlife
fences based on the research objective. Researchers stated the key features to
focus on while photographing the wildlife fences. The data collected contains
52 and 159 images from the drone and still cameras, respectively, as shown in
Table LABEL:tbl:raw_data. For both devices, we stored the photos in jpeg
format. Drone images have a height and width of 5904 and 3933 pixels,
respectively. And still, images have a height and width of 5904 and (3928 or
3936) pixels, respectively. Both of very high resolution as observed.

Table 1: RAW data collected on wildlife N2 fence and data augmented and used for training machine learning models Dataset name | Camera type | Fence type | Images | Description
---|---|---|---|---
Drone dataset | Drone |  Single fence | 26 |  Images of wildlife N2 single fences from drone camera
|  |  Double fence | 26 |  Images of wildlife N2 double fences from drone camera
Still dataset | Standalone |  Single fence | 79 |  Images of wildlife single fences from standalone camera
|  |  Double fence | 80 |  Images of wildlife double fences from standalone camera
Total |  |  | 211 |

###  4.3 Data preprocessing and annotation

The images were in image file format – RAW. This file type is uncompressed and
commonly captured by digital cameras and sensors. We processed the images from
RAW files to JPEG file format using Adobe Photoshop to convert the format and
filter the converted images.

The original dataset consists of 211 images collected using a standalone
camera and drone. For the classification task, the images are compressed to
equal dimensions of a width of 512 pixels and a height of 512 pixels. For the
two datasets, images from a drone and still cameras are saved in separate
folders. The still images contained only images that used a standalone camera;
the drone captured aerial images of wildlife fences and scenes from the sky.
The drone camera images used in this study are 26 from the single fence and 26
from the double fence. As well as 159 images from a standalone camera
containing 79 single and 80 double fence images as shown in Table
LABEL:tbl:raw_data. In Figure 3, we visualize a random sample of images from
our dataset and deploy it on mendeley [50]. Because of few samples, there was
a need to increase the size of the training images to improve model accuracy.
Data augmentation techniques are applied to expand on the sample of the
training dataset. The technique makes variations to the training sample,
avoids overfitting, and increases robustness [51]. Data augmentation
transformations applied in this study include horizontal flips, vertical
flips, random crops, and a small gaussian blur of about 50% of all images with
random sigma between 0 and 0.5, strengthening or weakening the contrast in
each image. We also added some gaussian noise per pixel and channel by
sampling only 50% of all images; this can change not only the color but also
the brightness of the pixels. We further made some images brighter and some
darker by sampling 20% of the images of all cases. We sampled the multiplier
once per channel, which ended up changing the color of the images. Finally, we
apply affine transformations to each image by scaling, zooming, translating,
moving, rotating, and shearing them. All the augmentations performed are
applied randomly.

Figure 3: Random samples from wildlife fences datasets for Classification Task

For the segmentation tasks, the original JPEG images have high-resolution
pixels as described in Section 4.2. In our observations, the wildlife fence
images contain a lot of noise, such as grass and trees, compared to the fence
itself. Besides, these images are enormous for machine learning models to
process. To minimize the noise, we cropped out noisy parts of the images to
$512\times 512$ pixels. The sliced images obtained contain both images with no
insulators (noise) and images with insulators. We discarded images with only
noise and retained images with insulators, as shown in Figure 4, where we
input the original images and output the cropped images. For each original
image, we reshape it to obtain an equal dimension image size for easy cropping
into smaller images, which are easily processed by the U-Net algorithm. We
then sort the photos into positive and negative regions. Further, we keep the
positive regions for further processing and discard the negative regions.

To build the ground truth mask of the insulators, we use the Visual Geometry
Group (VGG) Image Annotator (VIA) [52]. The VIA is used to define regions and
textual descriptions of the regions. The annotated images are saved in a .json
extension file containing the coordinates of the insulators. In this task,
each sliced image of a wildlife fence insulator is annotated to label
insulators as positive regions. The rest of the objects in the image are
labeled as negative regions. The positive regions are identified as 1, and the
negative region is identified as 0, which gives us a gray-scaled image as an
output or binary mask. The results from the annotations are image masks for
each insulator found on the wildlife fence. Since VIA output is smaller images
with a single insulator, to reconstruct a larger image containing all
insulators in an image, we concatenate them, as shown in Figure 5 and
Algorithm 1.

Figure 4: Split the original image into smaller images of $512\times 512$
pixels. Discard images with no insulators and retain images with insulators
for further processing

The generated pairs of $512\times 512$ pixel image masks with associated
original images, as shown in Figure 5 are fed into a neural network for model
training and fitting to learn to find the positive regions (insulators) on the
wildlife fences images. The annotated images are shown in Table
LABEL:tbl:processed_data. It includes 447 and 686 aerial and still images,
respectively, extracted from single and double fences. These are split into
the train, validation, and test sets. The still dataset contains 80% samples
for training, 10% for validation, and 10% for the test. In addition, drone
datasets have 80% images for training, 10% for validation, and 10% for the
test.

Figure 6 shows all the processes described above, i.e. from fieldwork for data
collection, to the implementation of the recognition and detection models then
the testing from field data.

Algorithm 1 An algorithm for image annotation and mask concatenation

Cropped mask $\leftarrow C_{m}$

Combined cropped mask $\leftarrow CC_{m}$

positive regions $\leftarrow P_{r}$

$P_{r}$

$C_{m},CC_{m}$

for each $P_{r}\in\\{1,2,3,4,5,6,....n\\}$ do

if $P_{r}$ == $512\times 512$ pixels then

Annotate $P_{r}\leftarrow C_{m},CC_{m}$

else

if $C_{m}$ == 1 then

width, height = $C_{m}$.size

$C_{m}$ = width, height

$C_{m}$ == $P_{r}$

return $C_{m}$

else

if $C_{m}>1$ then

$total\\_width=\sum(width)$

$total\\_height=\max(height)$

$CC_{m}$ = $C_{m}.new(total\\_width,total\\_height)$

$CC_{m}$ == $P_{r}$

return $CC_{m}$

end if

end if

end if

end for

##  5 Deep learning and optimization

###  5.1 Convolutional Neural Networks

Convolutional Neural Networks (CNN) consist of input and output layers. In
between these two layers, there are hidden convolutional layers, pooling
layers, and a fully connected layer. The layers are made up of neurons that
contain learnable weights and biases. Each neuron takes in several inputs,
computes a weighted sum by passing it through an activation function, and
returns an output [53]. CNN’s are used to classify images by learning
intrinsic image features, clustering these images by similarities, and
finally, performing object recognition. A CNN takes in images as input and
allocates importance by applying the learnable parameters (weights and biases)
to several features in the image, and by doing so, it can differentiate one
image from the other [54].

Figure 5: Input the sliced $512\times 512$ pixel image for annotation to generate individual masks for each insulator in the image, later we combined the individual masks to resemble the original image Figure 6: Proposed methodology: The process shows the proposed pipeline from the recognition and detection implementation to the test from field data. Table 2: Data reconstructed after slicing, containing images with insulators and annotated masks fed as input to the U-Net model Fence type | Images | Description
---|---|---
Drone | 447 | Wildlife fences insulator images and masks from drone camera
Still | 686 | Wildlife fences insulator images and masks from still camera
Total | 1033 |

###  5.2 Transfer Learning ResNets

This study leverages transfer learning using the wildlife fences dataset. An
approach is known for developing novel applications in image classification,
localization, detection, and segmentation tasks. To improve the performance of
CNNs, a transfer learning approach was introduced. Transfer learning is a
technique in machine learning where a model is pre-trained on a given dataset
and then re-used as a building block in a related task [55]. The reasoning
behind transfer learning is to use a model trained on a certain task to
improve learning in a related task of interest. Transfer learning has been
successful in many machine learning applications, including image
classification [56], image localization and detection [57] as well as image
segmentation [58], and has led to the development of CNN models that offer a
much greater performance [55, 59]. Examples of large transfer learning models
include VGG, GoogleNet (Inception), and Residual Network (ResNet). In this
study, we adopted ResNet50 architecture because of its power of skip
connections that eliminates the vanishing gradient problem. We draw upon these
works during model development and hyperparameter tuning and the overall fit
of the training data to each binary classification model, as it suggests that
model overfitting may be perilous to transferring learning to validation and
testing sets. The ResNet is developed to overcome some challenges experienced
when using the initially created transfer learning models. These common
challenges include problems with degradation, network optimization, and
vanishing or exploding gradient issues. ResNet50 model uses the concept of
shortcut or skipping in the CNN to carry out the task [60, 61]. ResNet has
shown great performance when applied to computer vision tasks.

###  5.3 U-Net for segmentation

The U-Net network architecture consists of an encoder (downsampler) and
decoder (upsampler) with a bottleneck connecting the encoder and decoder [24].
The skip connections concatenate encoder outputs to each stage of the decoder.
The decoder or contracting path contains a convolutional network with
alternating $3\times 3$ convolutions followed by ReLU activation unit as shown
in Equation 1 and max pooling operations as shown in Equation 2. Each stage
has an increasing number of filters, and the dimensionality of the features is
reduced because of the pooling layer. Later, more features are extracted by
the bottleneck. Finally, the expansion path or the decoder increases the
resolution of the output by upsampling the features using $2\times 2$ up-
convolution back to the original image size as shown in Equation 3. To
localize the object of interest, upsampled features, the expansive path
combines them with high-resolution features from the contracting path via skip
connections by cropping and concatenating the features. At each upsampling
level, we take the output of the corresponding encoder or contracting path and
concatenate it before feeding it to the next decoder or expansion path. We use
two successive $3\times 3$ convolutions and ReLU activation during upsampling.
At the final stage, we apply an additional $1\times 1$ convolution to reduce
the feature map to the required number of channels and produce the segmented
image as shown in Figure 7. During the expansion path, the cropping of images
is vital since pixel features in the edges have the least amount of contextual
information and therefore need to be discarded, which results in a network
resembling a u-shape. More importantly, it propagates contextual information
along the network, allowing it to segment objects in an area using context
from a larger overlapping area. The model output is a pixel-by-pixel mask
showing the class of each pixel.

$\displaystyle\mathbf{b}_{x,y,l}$
$\displaystyle=\mathrm{ReLu}\bigg{(}\sum_{i,j\in\\{-1,0,1\\}}\sum_{k}\mathbf{w}_{i,j,k,l}\cdot\mathbf{a}_{x+i,y+j,k}+\mathbf{c}_{l}\bigg{)}$
(1) $\displaystyle\mathbf{b}_{x,y,k}$
$\displaystyle=\max_{i,j\in\\{0,1\\}}\bigg{(}\mathbf{a}_{2x+i,2y+j,k}\bigg{)}$
(2) $\displaystyle\mathbf{b}_{2x+i,2y+i,l}$
$\displaystyle=\mathrm{ReLu}\bigg{(}\sum_{i,j\in\\{0,1\\}}\sum_{k}\mathbf{w}_{i,j,k,l}\cdot\mathbf{a}_{x,y,k}+\mathbf{c}_{l}\bigg{)},$
(3)

where $\mathbf{c}$ is the bias vector, $\mathbf{w}$ the weights matrix,
$\mathbf{a}$ the input feature map, $\mathbf{b}$ the output feature map. Here,
$x$ is the width, $y$ is the height, $k$ are the features index and $l$ is the
depth of image.

Figure 7: U-net architecture with the arrows represent the different
operations, the blue boxes represent the feature map at each layer, and the
light gray boxes represent the cropped feature maps from the contracting path
and dark gray represents the output image channels

###  5.4 Optimization via meta-learning

Since the dataset we used in this work is small, this justifies that we adopt
the transfer learning and a meta-learning type of optimization. The idea of
finding the optimized deep models $\Psi(\theta,\cdot)$ that could be
efficiently tested on a small dataset using meta-learning is as follows. From
the basic optimization problem, we want to minimize a loss function
$\mathcal{L}(y,\Psi(\theta,X))$ which uses the cross-entropy for
classification problems:

$\mathcal{L}(y,\Psi(\theta,X))=-\sum_{i}y_{i}\log\Psi(\theta,X_{i}),$ (4)

where the vector $y$ with components $y_{i}$ are know expected predictions,
and the vector $X$ with entries $X_{i}$ are features network inputs. Assuming
a sampled task, $\mathcal{T}\sim\rho(\mathcal{T})$ from the distribution of
tasks, $p(\mathcal{T})$. Split the entire dataset into training, validation,
and testing sub-datasets and randomly choose some $N$ samples of labels from
each of the classes and sequentially update the model weights vectors
$\theta^{j}_{i}$:

$\theta^{j}_{i}=\theta^{(j-1)}_{i}-\alpha\nabla_{\theta_{i}}\mathcal{L}(y_{\mathcal{S}_{i}},\Psi(\theta,X_{\mathcal{S}_{i}})),$
(5)

where $\alpha$ is the learning rate, $y_{\mathcal{S}_{i}}$ and
$X_{\mathcal{S}_{i}}$ are the expected input predictions sub-vector and input
features sub samples elements of the support set $\mathcal{S}$. Note that
$\theta^{0}_{i}\equiv\theta$. After the update of task-specific weight
$\theta^{j}_{i}$, the update of $\theta$ now properly follows

$\theta\leftarrow\theta-\beta\nabla_{\theta}\sum_{\mathcal{Q}_{i}\in\mathcal{T}_{i}}\mathcal{L}(y_{\mathcal{Q}_{i}},\Psi(\theta_{i}^{P},X_{\mathcal{Q}_{i}})).$
(6)

Here, $\beta$ is the learning rate, $y_{\mathcal{Q}_{i}}$ and
$X_{\mathcal{Q}_{i}}$ are the expected input prediction and feature vector
elements of the query set $\mathcal{Q}$. It can be seen that the update of
$\theta$ is an average of the objective function defined in Equation 4 across
the query set samples.

##  6 Evaluation metrics

###  6.1 Confusion matrix

The study solved a binary classification problem by evaluating the optimal
solution to predict whether the wildlife fences are either single fences or
double fences based on the characteristics of the wildlife fences. The
evaluations are done during training based on confusion matrix [62]. For the
confusion matrix, the table row shows the actual class, and the column
represents the predicted class. The confusion matrix shows the correspondence
between predicted and true values. The cells and columns of the confusion
matrix represent the correct and incorrect predictions with summarized count
values for each class for all the possible correlations. The cell in the
$i^{th}$ row and $j^{th}$ column means the percentage of the $i^{th}$ class
samples which are classified into the $j^{th}$ class. The diagonal cell of the
matrix contains the number of correctly identified pixels for each class. From
the confusion matrix in Table 3, $tp$ is when the actual class is positive and
the predicted class positive. An $fp$ is when the actual class is negative and
the predicted class positive. A $tn$ is when the actual class is negative and
the predicted class is negative. An $fn$ is when the actual class is positive
and the predicted class negative. Both $tp$ and $tn$ represent the number of
positive and negative instances that are correctly classified. While $fp$ and
$fn$ represent the number of misclassified negative and positive instances.

###  6.2 Intersection over Union (IoU)

IoU, or the Jaccard index, is a metric that determines the location of an
object in the image by computing the ground truth $g$ and predicted mask $p$
to determine the IoU. The ground truth is manually annotated on the image,
whereas the predicted mask is usually the output of a neural network. The IoU
is given as

$\displaystyle IoU=\frac{\lvert g\cup p\lvert}{\lvert g\cap
p\lvert}=\frac{tp}{tp+fp+fn},$ (7)

where $tp$ stands for true positive, $fp$ for false positive, $fn$ for false
negative. Further, we compute the Mean Intersection-Over-Union (MIoU), a
commonly used evaluation metric in determining the segmentation performance of
an image [63]. The metric is computed as follows:

$\displaystyle MIoU$
$\displaystyle=\frac{1}{c}\sum_{j=1}^{c}\frac{tp_{j}}{tp_{j}+fp_{j}+fn_{j}},$
(8)

where $c$ is the total number of classes, IoU always ranges between 0 and 1.
The ideal performance is 1, which means that the prediction is the same as
ground truth values and vice versa for 0. When the intersection area is
larger, IoU will be close to 1.

###  6.3 Dice similarity coefficient

The dice similarity coefficient metric is applied to a variety of datasets to
measure the similarity of two sets of data [64] and is used broadly in image
segmentation tasks. The metric is computed as:

$\displaystyle DSC(g,p)=\frac{2\times\lvert g\cap p\lvert}{\lvert
g\lvert+\lvert p\lvert}=\frac{2tp}{2tp+fp+fn},$ (9)

where $\lvert g\lvert$ and $\lvert p\lvert$ are the number of elements in $g$
and $p$, respectively. and the symbol $\cap$ represents the intersection of
two masks.

###  6.4 Accuracy, Recall, Precision, F1-Score and Support

Accuracy is the ratio of correct predictions out of the total of all
observations. This measure is the most intuitive. However, it is best when the
datasets are symmetric (i.e., values of $fp$ and $fn$ are almost the same):

$\displaystyle\mathrm{Accuracy}=\frac{tp+fn}{tp+fp+fn+tn},$ (10)

The recall is a measure of the classifier’s completeness, the ability of a
classifier to find all positive instances correctly. Each class is defined as
the ratio of true positives to the sum of true positives and false negatives:

$\displaystyle\mathrm{Recall}=\frac{tp}{fn+fn}.$ (11)

Precision is a measure of a classifier’s exactness. For each class, it is
defined as the ratio of true positives to the sum of true and false positives:

$\displaystyle\mathrm{Precision}=\frac{tp}{fn+tp}.$ (12)

The F1-score is a weighted harmonic mean of precision and recall such that the
best score is 1.0 and the worst is 0.0. Generally speaking, F1-scores are
lower than accuracy measures as they embed precision and recall into their
computation. As a rule of thumb, the weighted average of F1-score should be
used to compare classifier models, not global accuracy. F1-score is usually
more useful than accuracy when one have an uneven class distribution. Accuracy
works best if false positives and false negatives have similar costs. If the
cost of false positives and false negatives are very different, it is better
to look at both precision and recall:

$\displaystyle\mathrm{F1}\text{-}\mathrm{score}=\frac{2(rp)}{(r+p)}.$ (13)

Support is the number of actual occurrences of the class in the specified
dataset. Imbalanced support in the training data may indicate structural
weaknesses in the reported scores of the classifier and could indicate the
need for stratified sampling or rebalancing. Support does not change between
models but instead diagnoses the evaluation process.

Table 3: Confusion matrix used as a performance measurement for machine learning classification | Predicted Class |  |
---|---|---|---
Actual Class |  | Yes | No
Yes | True positive ($tp$) | False negative ($fn$)
No | False positive ($fp$) | True negative ($tn$)

##  7 Experiments and discussion

All experiments in this paper are performed using a computer equipped with an
Intel(R) Xeon(R) CPU E5-2695 v2 @ 2.40GHz computing unit, INTEL graphics INTEL
7 CPU for classification, segmentation, and detection tasks. We used the Keras
framework with a Tensorflow backend. Results using state-of-the-art networks
are compared for all datasets.

###  7.1 Recognition of wildlife fences using aerial and still datasets

Table LABEL:tbl:disk_size_classify shows the number of parameters for the
different classification architectures used in this paper. We observe that
models with smaller parameters outperformed models with a bigger number of
parameters. We also argue that models with smaller hard disk space are easily
deployed on mobile clients compared to bigger models, which are usually slow.
As for Table LABEL:tbl:classify_acc_loss_results, the CNN model achieved a
classification test accuracy of 73% in aerial images and 82% in still images.
However, the ResNet model showed a better performance on test set of 100% on
aerial images and 95% on still images. This is justified by the transferable
capabilities of ResNet from one adaptation domain to another, and their
robustness to operate on small data sets. Results indicate the CNN model
performed poorly compared to ResNet. However, results also show that CNN
performed better on aerial images than ResNet on still images. Therefore, we
recommend using transfer learning on the N2 wildlife fences dataset compared
to CNN’s because transfer learning architecture performs better than CNN. In
addition, the test accuracy using CNN is generally higher than train and
validation accuracy. We attribute this to the small dataset used in this study
and the inability of the CNN model to be robust compared to ResNet. Due to
these weaknesses, we computed more metrics such as confusion matrix,
precision, recall, F1-score, and support, with their associated average
accuracy, macro average, and weighted average.

For the confusion matrix in Figure 8 (a), (b), (c), and (d) correspond to the
results from aerial images with CNN, aerial images with ResNet, still images
with CNN, and still images with ResNet respectively. Figure 8 (a) shows 7
double fences classified as double fences and 1 double fence misclassified as
single fences, as well as 4 single fences classified as single fences and 3
single fences classified as double fences. For Figure 8 (b), we observe
classifications of 8 double fences and 7 single fences with no
misclassifications. Results of the confusion matrix when using still images
with CNN and ResNet achieved acceptable results with 23 double fences and 16
single images predicted correctly as double fences and single fences,
respectively. In the same Figure 8 (c), 7 single fences are predicted wrongly
as double fences, and 1 double fence is incorrectly classified as a single
fence. Finally, Figure 8 (c) results are from using still images and the
ResNet model. The model achieved correct predictions of 24 double fences and
21 single fences. The model also misclassified 2 single fences as double
fences, with no misclassifications for double fences.

Table 4: Data reconstructed for segmentation and detection tasks Dataset name | Model | Disk size (MBs) | Parameters
---|---|---|---
Aerial | CNN | 354.0 | 29,492,922
Aerial | ResNet | 101.3 | 24,125,208
Still | CNN | 393.3 | 32,769,882
Still | ResNet | 101.3 | 24,125,208

As in Table LABEL:tbl:cf(s), the CNN model achieved the classification average
accuracy of 73% on aerial images and 83% on still images. However, the ResNet
model performance is of 100% on aerial images as well as 96% on still images.
Generally, the classification models performed better on aerial images than
still images because the drone can capture wildlife fences from the front
view, back view, and top view, while the standalone camera captures only by
front view. Meaning drones capture more details of the fence and its
associated features as compared to a standalone camera. Based on these
results, we recommend the use of transfer learning on the wildlife fences
dataset as compared to CNN’s when using classification algorithms. We further
attribute the poor performance to the smaller datasets used for both aerial
and still.

Table 5: Results from the classification task for both aerial (dataset 1) dataset as well as still (dataset 2) dataset Dataset name | Model |  Train acc (80%) |  Train loss |  Val acc (10%) |  Val loss |  Test acc (10%) |  Test loss
---|---|---|---|---|---|---|---
Aerial | CNN | 74.81 | 0.6654 | 73.33 | 6579 | 73.33 | 0.7006
Aerial | ResNet | 100 | 4.6209e-06 | 100 | 0.0054 | 100 | 3.5681e-04
Still | CNN | 72.37 | 0.5996 | 61.70 | 1.1490 | 82.98 | 0.3767
Still | ResNet | 100 | 1.6472e-08 | 97.87 | 0.0275 | 95.74 | 0.3922

(a) Aerial, CNN

(b) Aerial, ResNet

(c) Still, CNN

(d) Still, ResNet

Figure 8: Four confusion matrix showing (a) images from drone classified using
CNN, (b) images from drone classified using ResNet, (c) images from standalone
camera classified using CNN, and (d) images from standalone camera classified
using ResNet

###  7.2 Prediction and detection of wildlife fence insulators

On average, 12 days and 2 hours are spent on training the aerial and still
datasets. During training, the loss, MIoU, accuracy, and dice coefficient are
calculated on the training and validation datasets at the end of each epoch.
And the parameters that got in the final epoch while training is considered
the training results. All the parameters of the training process get stored in
a dictionary, and those values are retrieved and used for plotting the graphs.
These graphs are used to evaluate the performance of the model. The model is
said to be good if the epoch vs accuracy graph is exponential and if the epoch
vs loss graph is a hyperbolic curve. Figures 9 and 10 show the loss, MIoU,
accuracy, and dice coefficient curves of the drone and still datasets,
respectively. The model results are in Table LABEL:tbl:results. In both
datasets, the loss curves show some slight oscillations when computing the
loss, accuracy, and dice coefficient metrics. This is related to the small
mini-batch size set in this study. The network cannot find the convergence
direction well at the early stage of training. When we observe the loss
curves, the network gradually becomes stable with continuous network training
obtaining results in Table LABEL:tbl:results.

Table 6: Aerial and Still datasets fine-tuned on CNN and ResNet binary classification algorithms. The wildlife single and double fences classes evaluations are precision, recall, F1-score, support, average accuracy, macro average, and weighted average. Avg metrics |  Dataset |  Model |  Fence type | Precision |  Recall |  F1-score |  Support
---|---|---|---|---|---|---|---
|  Aerial |  CNN |  double fence |  0.70 |  0.88 |  0.78 |  8
|  |  |  single fence |  0.80 |  0.57 |  0.67 |  7
Accuracy |  |  |  |  |  |  0.73 |  15
Macro Avg |  |  |  |  0.75 |  0.72 |  0.72 |  15
Weighted Avg |  |  |  |  0.75 |  0.73 |  0.73 |  15
|  Aerial |  ResNet |  double fence |  1.00 |  1.00 |  1.00 |  8
|  |  |  single fence |  1.00 |  1.00 |  1.00 |  7
Accuracy |  |  |  |  |  |  1.00 |  15
Macro Avg |  |  |  |  1.00 |  1.00 |  1.00 |  15
Weighted Avg |  |  |  |  1.00 |  1.00 |  1.00 |  15
|  Still |  CNN |  double fence |  0.77 |  0.96 |  0.85 |  24
|  |  |  single fence |  0.94 |  0.70 |  0.80 |  23
Accuracy |  |  |  |  |  |  0.83 |  47
Macro Avg |  |  |  |  0.85 |  0.83 |  0.83 |  47
Weighted Avg |  |  |  |  0.85 |  0.83 |  0.83 |  47
|  Still |  ResNet |  double fence |  0.92 |  1.00 |  0.96 |  24
|  |  |  single fence |  1.00 |  0.91 |  0.95 |  23
Accuracy |  |  |  |  |  |  0.96 |  47
Macro Avg |  |  |  |  0.96 |  0.96 |  0.96 |  47
Weighted Avg |  |  |  |  0.96 |  0.96 |  0.96 |  47

###  7.3 Mask prediction accuracy

We applied the U-Net model to the wildlife aerial image dataset and wildlife
still dataset, which is labeled as insulators and not insulators. Every image
of the data is RGB and has 512 $\times$ 512 pixels resolution. The best MIoU
are recorded for training, validation, and testing for both aerial and still
datasets in Table LABEL:tbl:results. Adam with learning is adopted as an
optimization algorithm [65].

Figure 9:  Loss, MIoU, accuracy and dice curves of the drone image dataset
model Figure 10:  Loss, MIoU, accuracy and dice curves of the still image
dataset model

The output of the given neural network contains an image where each pixel
corresponds to a probability to detect interested area. The size of the output
image coincides with the input image. To have only binary pixel values, we
choose a threshold of 0.5. For all pixel values below the specified threshold,
we set it to zero, while for all values above the threshold, we set it to 1.
We then multiplied the results of every pixel in an output image by 255 and
got a black-and-white predicted mask. Further, for each predicted ground truth
mask, we calculate the residuals. The smaller the residuals, the better the
model performance, and the more the residuals, the poor the model performance,
as shown in Figure 11

Table 7: Prediction results on training, validation and testing dataset samples. Still, dataset performed better on small batches than on bigger batches. The aerial slightly performed better on the 16 batch size than the 8 batch size. Generally, the model performed better on the drone dataset than still images. Dataset |  Dataset sample |  Batch size | Loss |  Mean IoU |  Accuracy |  Dice coef
---|---|---|---|---|---|---
Drone |  Train |  16 |  -0.6024 |  0.6776 |  0.9980 |  0.6361
|  |  8 |  -0.5492 |  0.6442 |  0.9974 |  0.5768
|  Val |  16 |  -0.7097 |  0.7322 |  0.9987 |  0.7066
|  |  8 |  -0.5595 |  0.6989 |  0.9977 |  0.5793
|  Test |  16 |  -0.6279 |  0.7345 |  0.9982 |  0.6771
|  |  8 |  -0.5938 |  0.6992 |  0.9981 |  0.6378
Still |  Train |  16 |  22.7265 |  0.4602 |  0.9684 |  0.0053
|  |  8 |  -0.1184 |  0.5017 |  0.9646 |  0.1872
|  Val |  16 |  22.7265 |  0.4857 |  0.9698 |  0.0097
|  |  8 |  -0.0826 |  0.5405 |  0.9678 |  0.1644
|  Test |  16 |  19.7826 |  0.4856 |  0.9740 |  0.0047
|  |  8 |  -0.0580 |  0.5388 |  0.9704 |  0.1069

###  7.4 Insulator detection with OpenCV

OpenCV is an open-source library widely used for image analysis, recognition,
and deep learning. The software is used exhaustively to recognize objects in
photos and videos. Object detection is a subset of signal processing, big
data, and machine learning focusing on identifying features in pictures and
videos. Images are labeled as positive and negative to train a cascade
function, which is used to detect objects in images [66]. In this work, we
detect insulators using OpenCV as shown in Figure 12 from the aerial dataset
by finding contours defined by joining lines of all points at the image
boundary with the same intensity. OpenCV uses a function findContours() to
extract contours from an image that meets a specified threshold. In addition,
a boundingRect() function is adopted to approximate rectangles of the blobs on
an image to show the area of interest after getting the image’s contours or
outer shape. This function returns four values, the x-coordinate,
y-coordinate, width, and height of the blob. These values are used to draw a
rectangle on the image part using pixel coordinates. A few pixels are added to
these values to draw a slightly bigger box. These coordinates from mask images
are inferred into the actual images to draw the borders around them. The cut-
outs images and their corresponding mask images are passed through our OpenCV
code to detect all the sources in the image. Later these are concatenated back
to an original image of which borders are sketched around it, as shown in
Figures 12 and 13, applied to the drone and still images respectively.

Figure 11: Representation of actual images, ground truth masks, predicted
masks, and residual masks on aerial dataset Figure 12: Detected insulators on
wildlife electric fence at Amakhala private game reserve from the drone
dataset Figure 13: Detected insulators on wildlife electric fence at Amakhala
private game reserve from the still dataset

###  7.5 Discussion

Wildlife-vehicle collision incidences are high in South Africa in areas with
wildlife fences [67]. This prompts research on how to minimize and prevent
wildlife collisions with cars and ensure the safety of drivers, pedestrians,
and other road users. Ecologists and wildlife conservationists in Africa want
an easy way to detect WVC areas with high accuracy and low cost while
minimizing the weaknesses of roadkill prediction using time series analysis,
an approach currently adopted in road ecology in detecting roadkill areas. To
answer the ecologists, this study automatically investigates the possibility
of the U-Net segmentation algorithm, CNN, and ResNet in detecting and
recognizing WVC areas. It does this by understanding the performance of CNN,
ResNet, and U-Net trained on still and aerial wildlife fence and wildlife
fence insulator datasets. Ultimately, for segmentation, it verifies if this is
a good approach for detecting WVC fences by further drawing bounding boxes on
the predicted insulators as shown in Figure 12. In addition, the ResNet and
CNN recognize WVC areas using wildlife single and double fences shown on the
confusion matrix in Figure 8 as well as precision, recall, F1-score, and
support in Table LABEL:tbl:cf(s). Generally, deep learning models’ accuracy is
correlated to the size of the dataset; the bigger the dataset, the higher the
accuracy and vice-versa [68, 69]. This is un-realist in an ecological sense
since large amounts of training data can not be easily produced by ecological
researchers. For that purpose, we designed the U-Net model for smaller
ecological wildlife fences insulator datasets. And adopted ResNet for the
classification task, which performs better than CNN.

Our model results in Table LABEL:tbl:results show that the U-Net model
performs better on the aerial dataset than the still dataset, even though the
aerial dataset is small. This is partially true due to the variations in
specific camera parameters, which are known to affect the performance and
generalization of deep learning models [70]. The two camera devices, DJI
Phantom 4 Pro UAV drone and Nikon Z6 Mk 2 still camera, have different
parameters affecting the U-Net, CNN, and ResNet results. Also, the drone
captures more details (front, back, and top views) for machine learning models
to learn compared to the still camera, which captures only the front view. In
addition, the images are over-zoomed or decompressed, leading to poor
performance since we have much information lost.

Our work can uniquely combine ecological wildlife fences, wildlife fence
insulator data, and deep learning techniques. The field of image recognition,
especially when applied to wildlife fencing in detecting WVC areas, is still
young. While most researchers have used similar methodologies, they have used
them in different fields but not in detecting WVC areas using wildlife fences
to minimize WVC.

Our results demonstrate the successful capabilities of ResNet, CNN, and U-Net
within the ecological domain, although with identified limitations. The
algorithms, if deployed, can be a powerful and practical tool that helps
ecologists extract ecological information from the wildlife fencing algorithm
deployed.

###  7.6 Future Scope

We offer a few possibilities for future work by combining traditional wildlife
fencing, classification, and segmentation algorithms.

First, we acknowledge that our datasets are not distributed. It comprises only
two game reserves in Grahamstown, Eastern Cape, South Africa. As a result,
future work should repeat the data collection process by representing more WVC
areas in different provinces of South Africa and testing the model’s
generalization between the different identified locations. This will improve
machine learning systems in generalizing their training data, which makes the
model better in generalizing [71].

Second, deploying machine learning models to the client devices is vital, as
periodically retraining the model from model staleness. Deployment has been
successful in public policy [72], health [73], education [74], and banking
[75]

Thirdly, to further improve the classification accuracy of remote sensing,
adopting image localization and detection algorithms might help to extract the
location of an object in an image using algorithms such as histogram of
oriented gradients, YOLO, Region-based Convolutional Neural Networks (R-CNN),
Fast R-CNN, and Faster R-CNN.

Fourthly, Building more images of the dataset using synthetic images will
improve performance by exponentially increasing the number of example images a
model sees during training. This approach is widely used by U-Net with
generative adversarial networks and is successful [76, 77]

Fifthly, models developed using aerial and still datasets may be continuously
trained and deployed to minimize model staleness by using proactive training
[78]. This approach will improve model performance and reliability by
retraining the images the model provided as incorrect predictions.

Lastly, we believe the future of deep learning models relies upon developing
an end-to-end WVC fence detection system to monitor and detect WVC areas. The
system should detect wildlife fences as WVC areas and alert the driver.
Further, the system should allow the driver to save images and GPS. The
collected image dataset can be used to retrain the model. And the GPS will
help improve the existing roadkill areas detection system by detecting future
patterns and distribution of WVC areas using time series analysis.

##  8 Conclusion

The study aimed to evaluate the model performance of modern computer vision
algorithms applied to aerial and still image datasets. For this purpose, we
utilized drones and standalone cameras to obtain images of wildlife fences on
N2 highways. These images were sorted as single and double fences, each
associated with the device from which they originate, i.e., drone or
standalone camera. For the classification task, the images were labeled and
fed as input to the computer vision algorithms to recognize fences. In
addition to the dataset contribution, we also conducted a comparative study on
state-of-the-art image-based classification algorithms and reported our
experimental results in this paper. We exploited CNN and ResNet50 architecture
for the recognition tasks. The recognition task detects the test image as a
single or double fence.

For the segmentation task, our contribution is the dataset that contains
segmented images and masks for wildlife electric fence insulators. Our
proposed approach to detecting fences facilitates the development of image
segmentation algorithms with wildlife fences and their associated features.
This is done by annotating the dataset, which increases the efficiency of
machine learning projects in detecting areas of interest. Detecting wildlife
fence features such as insulators is essential in mitigating WVC.

Comparisons of CNN results and those from the ResNet50 architecture showed
that ResNet50 outperforms CNN when applied to aerial and still images.
Generally, the classification and U-Net models performed better on aerial
images than still images because the drone can capture wildlife fences by the
front view, back view, and top view, while the standalone camera captures only
by front view. Meaning drones capture more details of the fence and its
associated features as compared to a standalone camera. Besides, image
features from the still camera are over-decompressed compared to images from a
drone camera. That means more information is lost, which has contributed to
the weak performance of these models.

In the future, we plan to gradually enlarge our dataset for single and double
fences using drone and standalone devices. We hope making public our dataset
to foster future image-based wildlife fence recognition research. Further, we
will minimize the decompression of images, detect other vital features of
wildlife electric fences, adopt localization and detection algorithms to
detect the location of objects in an image, and deploy the algorithms on
client devices. Besides, we shall also adopt using other segmentation
techniques.

## Disclosure statement

No potential conflict of interest was reported by the author(s)

## Ethical clearance

Ethical review and approval were waived for this study by the Animal Research
Ethics Committee of Rhodes University, South Africa, because the study did not
involve the use of live animals and therefore did not require ethical
clearance.

## Funding

This study was carried out with financial support from Rhodes University.

## Data availability

The data that support the findings of this study are accessible from the
corresponding author upon request.

## Acknowledgment

The authors would like to thank Amkhala and Lalibela game reserves in South
Africa for many valuable discussions and educational help in wildlife fence
features, on how wildlife fence works, and for allowing us to take photographs
of the wildlife fence on highways. The authors also express sincere gratitude
to HLB Photography 111https://www.hlbphotography.co.za/ for providing us with
a photographer and drone pilot to capture pictures.

## Authors’ contributions

Conceptualization, I.N. and M.A.; methodology, I.N. and M.A.; data collection
I.N., M.A. and P.O.; data curation, I.N.; software, I.N.; validation, I.N.,
M.A. and M.N.; formal analysis, I.N., M.A., P.O., M.N., J.L.E.K.F. and F.T.;
investigation, I.N.; writing—original draft preparation, I.N.; writing—review
and editing, I.N., M.A., P.O., M.N., J.L.E.K.F. and F.T.; visualization, I.N.,
M.A., P.O., J.L.E.K.F. and F.T.; supervision, M.A. and P.O.; editing the
document with an ecological audience in mind, I.N. and M.N.; project
administration, I.N. and M.A.; funding acquisition, M.A. All authors wrote the
manuscript, commented and improved it. All authors read and approved the final
version of the manuscript.

## References

  * [1] A. Pekor, J. R. Miller, M. V. Flyman, S. Kasiki, M. K. Kesch, S. M. Miller, K. Uiseb, V. van der Merve, P. A. Lindsey, Fencing africa’s protected areas: Costs, benefits, and management issues, Biological Conservation 229 (2019) 67–75.  doi:https://doi.org/10.1016/j.biocon.2018.10.030.
URL https://www.sciencedirect.com/science/article/pii/S0006320718306578

  * [2] S. M. Durant, M. S. Becker, S. Creel, S. Bashir, A. J. Dickman, R. C. Beudels-Jamar, L. Lichtenfeld, R. Hilborn, J. Wall, G. Wittemyer, L. Badamjav, S. Blake, L. Boitani, C. Breitenmoser, F. Broekhuis, D. Christianson, G. Cozzi, T. R. B. Davenport, J. Deutsch, P. Devillers, L. Dollar, S. Dolrenry, I. Douglas-Hamilton, E. Dröge, E. FitzHerbert, C. Foley, L. Hazzah, J. G. C. Hopcraft, D. Ikanda, A. Jacobson, D. Joubert, M. J. Kelly, J. Milanzi, N. Mitchell, J. M’Soka, M. Msuha, T. Mweetwa, J. Nyahongo, E. Rosenblatt, P. Schuette, C. Sillero-Zubiri, A. R. E. Sinclair, M. R. Stanley Price, A. Zimmermann, N. Pettorelli, Developing fencing policies for dryland ecosystems, Journal of Applied Ecology 52 (3) (2015) 544–551.  doi:https://doi.org/10.1111/1365-2664.12415.
URL
https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.12415

  * [3] W. Hanophy, Fencing with wildlife in mind, XColorado Division of Wildlife, 2009\.
  * [4] S. A. L. COMMISSION, Uniform national legislation on the fencing of national roads, https://www.gov.za/sites/default/files/gcis_document/201409/ip16prj1192000.pdf, Accessed 15 September 2022 (2000).
  * [5] J. Kioko, C. Kiffner, N. Jenkins, W. J. Collinson, Wildlife roadkill patterns on a major highway in northern tanzania, African Zoology 50 (2015) 17–22.
  * [6] A. Almeida, G. Azkune, Predicting human behaviour with recurrent neural networks, Applied Sciences 8.  doi:https://doi.org/10.3390/app8020305.
  * [7] I. Nandutu, M. Atemkeng, N. Mgqatsa, S. Toadoum Sari, P. Okouma, R. Rockefeller, T. Ansah-Narh, J. L. Ebongue Kedieng Fendji, F. Tchakounte, Error correction based deep neural networks for modeling and predicting south african wildlife–vehicle collision data, Mathematics 10 (21) (2022) 3988.
  * [8] T. White, Defenders of wildlife. watch out for wildlife facts, https://defenders.org/sites/default/files/publications/collision_facts_and_figures.pdf, Accessed 5 June 2020 (2020).
  * [9] C. Tony, C. Brian L, F. Adam, H. Marcel, L. Bruce F, W. Bethanie, W. Chuck, Wildlife-vehicle collision reduction study: Report to congress, Tech. rep., U.S Department of Transportation (2008).
  * [10] F. Heigl, K. Horvath, G. e. a. Laaha, Amphibian and reptile road-kills on tertiary roads in relation to landscape structure: using a citizen science approach with open-access land cover data, BMC Ecol 17.  doi:https://doi.org/10.1186/s12898-017-0134-z.
  * [11] W. Collision, Roadkill study highlights wildlife road deaths, https://www.bridgestone.co.za/news-article/698/roadkill-study-highlights-wildlife-road-deaths,Accessed 23 April 2020 (2012).
  * [12] Wheels24, Roadkill: Why so many animals die on sa’s roads, https://www.wheels24.co.za/News/Roadkill-in-SA-Distraction-not-speed-to-blame-20150422,Accessed 9 April 2020 (2015).
  * [13] W. Collinson, H. Davies-Mostert, W. Davies-Mostert, Effects of culverts and roadside fencing on the rate of roadkill of small terrestrial vertebrates in northern limpopo, south africa, Conservation Evidence 14 (2017) 39–43.
  * [14] I. Nandutu, M. Atemkeng, P. Okouma, Intelligent systems using sensors and/or machine learning to mitigate wildlife-vehicle collisions: A review, challenges, and new perspectives, Sensors 22 (7).  doi:10.3390/s22072478.
URL https://www.mdpi.com/1424-8220/22/7/2478

  * [15] M. P. Huijser, P. T. McGowen, W. Camel, et al., Animal vehicle crash mitigation using advanced technology phase i: review, design, and implementation., Tech. rep., Montana State University (2006).
  * [16] M. Desholm, Thermal animal detection system (tads). development of a method for estimating collision frequency of migrating birds at offshore wind turbines, Tech. rep., National Environmental Research Institute (2003).
  * [17] M. P. Huijser, C. Haas, K. R. Crooks, et al., The reliability and effectiveness of an electromagnetic animal detection and driver warning system., Tech. rep., Colorado. Dept. of Transportation. Research Branch (2012).
  * [18] C. Catlett, E. Cesario, D. Talia, A. Vinci, Spatio-temporal crime predictions in smart cities: A data-driven approach and experiments, Pervasive and Mobile Computing 53 (2019) 62–74.  doi:https://doi.org/10.1016/j.pmcj.2019.01.003.
URL https://www.sciencedirect.com/science/article/pii/S157411921830542X

  * [19] U. Butt, S. Letchmunan, F. Hassan, M. Ali, A. Baqir, T. W. Koh, H. Sherazi, Spatio-temporal crime predictions by leveraging artificial intelligence for citizens security in smart citiesdoi:10.20944/preprints202102.0172.v1.
  * [20] P. HP-Ruffino, K. G. Rodríguez-C, L. A. Ramazzotto, P. D. Freitas, et al., Roadkill hotspots in a protected area of cerrado in brazil: planning actions to conservation, Revista MVZ Córdoba 21 (2) (2016) 5441–5448.
  * [21] A. U. ÖZCAN, N. K. ÖZKAZANÇ, Identifying the hotspots of wildlife-vehicle collision on the çankırı-kırıkkale highway during summer, Turkish Journal of Zoology 41 (4) (2017) 722–730.
  * [22] W. Collinson, D. Parker, R. Bernard, B. Reilly, H. Davies-Mostert, An inventory of vertebrate roadkill in the greater mapungubwe transfrontier conservation area, south africa, African Journal of Wildlife Research 45 (2015) 301–311.  doi:https://doi.org/10.3957/056.045.0301.
  * [23] Y. Liu, S. Zhang, H. Yu, Y. Wang, Y. Feng, J. Sun, X. Zhou, Straw segmentation algorithm based on modified unet in complex farmland environment, Computers, Materials & Continua 66 (2020) 247–262.  doi:10.32604/cmc.2020.012328.
  * [24] O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer, 2015, pp. 234–241.
  * [25] G. Bradski, A. Kaehler, Learning OpenCV: Computer vision with the OpenCV library, ” O’Reilly Media, Inc.”, 2008.
  * [26] C. R. Goeldner, J. B. Ritchie, Tourism principles, practices, philosophies, John Wiley & Sons, 2007.
  * [27] R. of South Africa, White paper on the development and promotion of tourism in south africa (1996).
  * [28] Y. Mansfeld, A. Pizam, Tourism, security and safety: From theory to practice (the management of hospitality and tourism enterprises).
  * [29] W. cape government, Tourism safety strategy. destination western cape. creating an environment for economic growth and jobs, Tech. rep., Western cape government, Economic development, and tourism (2022).
  * [30] A. G. H. E. Oddone Aquino, S. L. Nkomo, Spatio-temporal patterns and consequences of road kills: a review, Animals 11 (3) (2021) 799.
  * [31] P. H. Marcel, D. H. Tiffany, B. Matt, C. G. Mark, T. M. Pat, H. Barrett, W. Shaowei, The comparison of animal detection systems in a test-bed: A quantitative comparison of system reliability and experiences with operation and maintenance, Tech. rep., Federal Highway Administration and Montana Department of Transportation (2009).
  * [32] W. H. S. Antônio, M. D. Silva, R. S. Miani, J. R. Souza, A proposal of an animal detection system using machine learning, Applied Artificial Intelligence 33 (2019) 1093–1106.
  * [33] M. P. Huijser, L. Hayden, Evaluation of the reliability of an animal detection system in a test-bed, Tech. rep., Western Transportation Institute (2010).
  * [34] D. Smith, M. Grace, A. Miller, M. Noss, R. Noss, Assessing the effectiveness and reliability of the roadside animal detection system on us highway 41 near the turner river in collier county. final report. contract no. bdv37, two #2., Tech. rep., Florida Department of Transportation, District One, Bartow, FL. 72 pp. + appendices (2016).
  * [35] V. Shapoval, J. Lev, J. Bartoška, F. Kumhála, Application of doppler radar for wildlife detection in vegetation, Scientia Agriculturae Bohemica 49 (2018) 136–141.
  * [36] M. P. Huijser, E. R. Fairbank, F. D. Abra, et al., The reliability and effectiveness of a radar-based animal detection system, Tech. rep., University of Alaska Fairbanks. Center for Environmentally Sustainable (2017).
  * [37] B. Vikhram, B. Revathi, R. Shanmugapriya, S. Sowmiya, G. Pragadeeswaran, Animal detection system in farm areas, International Journal of Advanced Research in Computer and Communication Engineering 6 (2017) 587–591.
  * [38] A. Mukherjee, A. Sullivan, A. Sinha, X. Liu, D. Brake, Roadway monitoring and driver warning systems for wildlife-vehicle collision avoidance, Tech. rep., AUG Signals Ltd (2013).
  * [39] C. Druta, A. S. Alden, et al., Evaluation of a buried cable roadside animal detection system., Tech. rep., Virginia Center for Transportation Innovation and Research (2015).
  * [40] C. Druta, A. S. Alden, Preventing animal-vehicle crashes using a smart detection technology and warning system, Transportation research record 2674 (2020) 680–689.
  * [41] S. Sharma, D. Shah, Real-time automatic obstacle detection and alert system for driver assistance on indian roads, International Journal of Vehicle Autonomous Systems 13 (3) (2017) 189–202.
  * [42] D. L. Rosenband, Inside waymo’s self-driving car: My favorite transistors, in: 2017 Symposium on VLSI Circuits, IEEE, 2017, pp. C20–C22.
  * [43] N. Sillero, R. Hélder, F. Marc, S. Cristiano, L. Gil, A road mobile mapping device for supervised classification of amphibians on roads, European Journal of Wildlife Research 64.  doi:https://doi.org/10.1007/s10344-018-1236-4.
  * [44] D. Sousa Guedes, H. Ribeiro, N. Sillero, An improved mobile mapping system to detect road-killed amphibians and small birds, ISPRS International Journal of Geo-Information 8.  doi:https://doi.org/10.3390/ijgi8120565.
  * [45] N. Hallisey, S. W. Buchanan, B. D. Gerber, L. S. Corcoran, N. E. Karraker, Estimating road mortality hotspots while accounting for imperfect detection: A case study with amphibians and reptiles, Land 11 (5) (2022) 739.
  * [46] I. Nandutu, M. Atemkeng, P. Okouma, Integrating ai ethics in wildlife conservation ai systems in south africa: A review, challenges, and future research agenda, AI & SOCIETY (2021) 1–13.
  * [47] J. W. Gagnon, C. D. Loberger, S. C. Sprague, K. S. Ogren, S. L. Boe, R. E. Schweinsburg, Cost-effective approach to reducing collisions with elk by fencing between existing highway structures, Human–Wildlife Interactions 9 (2) (2015) 14.
  * [48] A. Game, F. Department, Wildlife compatible fencing, https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcseprd1080807&ext=pdf, Accessed 11 July 2022 (2022).
  * [49] J. W. Worley, Fences for the firm, https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs141p2_023913.pdf, Accessed 11 July 2022 (2022).
  * [50] M. Atemkeng, I. Nandutu, P. Okouma, A dissected dataset for single and double wildlife fences in south africa (under review), Data in Brief.
  * [51] C. Shorten, T. M. Khoshgoftaar, A survey on image data augmentation for deep learning, Journal of big data 6 (2019) 1–48.
  * [52] A. Dutta, A. Zisserman, The via annotation software for images, audio and video, in: Proceedings of the 27th ACM international conference on multimedia, 2019, pp. 2276–2279.
  * [53] S. Albawi, T. A. Mohammed, S. Al-Zawi, Understanding of a convolutional neural network, in: 2017 international conference on engineering and technology (ICET), Ieee, 2017, pp. 1–6.
  * [54] K. O’Shea, R. Nash, An introduction to convolutional neural networks, arXiv preprint arXiv:1511.08458.
  * [55] R. Saha, Transfer learning–a comparative analysis (2018).
  * [56] Y. Zhu, Y. Chen, Z. Lu, S. J. Pan, G.-R. Xue, Y. Yu, Q. Yang, Heterogeneous transfer learning for image classification, in: Twenty-fifth aaai conference on artificial intelligence, 2011.
  * [57] H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, R. M. Summers, Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning, IEEE transactions on medical imaging 35 (5) (2016) 1285–1298.
  * [58] A. Van Opbroek, M. A. Ikram, M. W. Vernooij, M. De Bruijne, Transfer learning improves supervised image segmentation across imaging protocols, IEEE transactions on medical imaging 34 (5) (2014) 1018–1030.
  * [59] K. Weiss, T. M. Khoshgoftaar, D. Wang, A survey of transfer learning, vol. 3, no. 1 (2016).
  * [60] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  * [61] K. He, X. Zhang, S. Ren, J. Sun, Identity mappings in deep residual networks, in: European conference on computer vision, Springer, 2016, pp. 630–645.
  * [62] M. Hossin, M. N. Sulaiman, A review on evaluation metrics for data classification evaluations, International journal of data mining & knowledge management process 5 (2) (2015) 1.
  * [63] M.-Y. Li, D.-J. Zhu, W. Xu, Y.-J. Lin, K.-L. Yung, A. W. Ip, Application of u-net with global convolution network module in computer-aided tongue diagnosis, Journal of Healthcare Engineering 2021.
  * [64] K. H. Zou, S. K. Warfield, A. Bharatha, C. M. Tempany, M. R. Kaus, S. J. Haker, W. M. Wells III, F. A. Jolesz, R. Kikinis, Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports, Academic radiology 11 (2) (2004) 178–189.
  * [65] D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980.
  * [66] P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, in: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, Vol. 1, Ieee, 2001, pp. I–I.
  * [67] P. Eloff, A. Van Niekerk, Temporal patterns of animal-related traffic accidents in the eastern cape, south africa, South African Journal of Wildlife Research-24-month delayed open access 38 (2) (2008) 153–162.
  * [68] M. A. Tabak, M. S. Norouzzadeh, D. W. Wolfson, S. J. Sweeney, K. C. VerCauteren, N. P. Snow, J. M. Halseth, P. A. Di Salvo, J. S. Lewis, M. D. White, et al., Machine learning to classify animal species in camera trap images: Applications in ecology, Methods in Ecology and Evolution 10 (4) (2019) 585–590.
  * [69] M. Willi, R. T. Pitman, A. W. Cardoso, C. Locke, A. Swanson, A. Boyer, M. Veldthuis, L. Fortson, Identifying animal species in camera trap images using deep learning and citizen science, Methods in Ecology and Evolution 10 (1) (2019) 80–91.
  * [70] Z. Liu, T. Lian, J. Farrell, B. Wandell, Neural network generalization: The impact of camera parameters, IEEE Access PP (2020) 1–1.  doi:10.1109/ACCESS.2020.2965089.
  * [71] Y. LeCun, Y. Bengio, G. Hinton, Deep learning, nature 521 (7553) (2015) 436–444.
  * [72] K. Ackermann, J. Walsh, A. De Unánue, H. Naveed, A. Navarrete Rivera, S.-J. Lee, J. Bennett, M. Defoe, C. Cody, L. Haynes, et al., Deploying machine learning models for public policy: A framework, in: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 15–22.
  * [73] V. Kulkarni, M. Gawali, A. Kharat, et al., Key technology considerations in developing and deploying machine learning models in clinical radiology practice, JMIR Medical Informatics 9 (9) (2021) e28776.
  * [74] Y. Takabe, M. Uehara, Rapid deployment for machine learning in educational cloud, in: 2013 16th International Conference on Network-Based Information Systems, IEEE, 2013, pp. 372–376.
  * [75] M. Tabiaa, A. Madani, The deployment of machine learning in ebanking: A survey., in: 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS), IEEE, 2019, pp. 1–7.
  * [76] E. Schonfeld, B. Schiele, A. Khoreva, A u-net based discriminator for generative adversarial networks, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 8207–8216.
  * [77] C. Wu, Y. Zou, Z. Yang, U-gan: generative adversarial networks with u-net for retinal vessel segmentation, in: 2019 14th International Conference on Computer Science & Education (ICCSE), IEEE, 2019, pp. 642–646.
  * [78] I. Prapas, B. Derakhshan, A. R. Mahdiraji, V. Markl, Continuous training and deployment of deep learning models, Datenbank-Spektrum 21 (3) (2021) 203–212.