# Recognition of Russian traffic signs in winter conditions. Solutions of the
“Ice Vision” competition winners.

Artem L. Pavlov1, Azat Davletshin2, Alexey Kharlamov3, Maksim S. Koriukin4,
Artem Vasenin2, Pavel Solovev3,
Pavel Ostyakov3, Pavel A. Karpyshev1, George V. Ovchinnikov5, Ivan V.
Oseledets6, and Dzmitry Tsetserukou6 1A. L. Pavlov and P. A. Karpyshev are PhD
students with the Space Center, Skolkovo Institute of Science and Technology
(Skoltech), Moscow, Russia<EMAIL_ADDRESS>Davletshin and A.
Vasenin are ML Engineers with the NtechLab company.
<EMAIL_ADDRESS>Kharlamov, P. Solovev and P. Ostyakov are
Research Scientists with the Samsung AI Center, Moscow.4M. S. Koriukin is a
PhD student with the Department of System Analysis, Institute of Computer
Science and Telecommunications, Reshetnev Siberian State University of Science
and Technology, Krasnoyarsk, Russia.5G. V. Ovchinnikov is a Research Scientist
with the Center for Computational and Data-Intensive Science and Engineering
(CDISE), Skoltech.6I. V. Oseledets and D. Tsetserukou are Full and Associate
Professors with the CDISE and the Space Center respectively, Skoltech.

###### Abstract

With the advancements of various autonomous car projects aiming to achieve SAE
Level 5, real-time detection of traffic signs in real-life scenarios has
become a highly relevant problem for the industry. Even though a great
progress has been achieved in this field, there is still no clear consensus on
what the state-of-the-art in this field is.

Moreover, it is important to develop and test systems in various regions and
conditions. This is why the “Ice Vision” competition has focused on the
detection of Russian traffic signs in winter conditions. The IceVisionSet
dataset used for this competition features real-world collection of lossless
frame sequences with traffic sign annotations. The sequences were collected in
varying conditions, including: different weather, camera exposure,
illumination and moving speeds.

In this work we describe the competition and present the solutions of the 3
top teams.

##  I Introduction

The recognition of traffic signs is a multi-category classification problem
with unbalanced class frequencies. It is a highly relevant industrial problem,
with autonomous driving being the most prominent application of the
technology. While being simpler than many other computer vision problems due
to the availability of reference images, it has significant reliability
requirements, since false detections may cause incorrect behavior of an
autonomous car, which may end in a traffic accident.

Additionally, while traffic signs show a wide range of variations between
classes in terms of color, shape, and the presence of pictograms or text, some
classes contain important information which must be recognized for sign
detection to be useful, e.g. speed limit or distance information. Other signs
change their meaning depending on orientation, e.g. pedestrian crossing or
turn left/right signs. In other words, subtle differences in signs can have a
significant impact on decision making.

Moreover, algorithms have to cope with large variations in visual appearance
due to illumination changes, partial occlusions, rotations, weather
conditions, scaling, etc. And they have to deal with a large number of highly
unbalanced classes, e.g. in Russia there are almost 300 sign classes, not
counting variations inside class (i.e. speed limit is counted as a single
class). Plus the number of classes rises fast if solution is intended to be
used in many countries, most of which have different traffic signs.

According to the survey by Paclik [1], the works on traffic sign detection and
recognition started as early as in 1984. A great deal of excellent algorithms
for traffic sign detection was proposed since then. Detailed reviews on this
topic can be found in [2] and [3].

Figure 1: Frame examples from the IceVisionSet dataset.

Figure 2: Annotated sign examples from the IceVisionSet dataset.

Deep learning approaches are overwhelmingly popular amongst the recent works
in this field. Having large datasets is of the utmost importance for training
such solutions. A great number of traffic sign datasets for different
countries has been previously published, including: the Belgian Traffic Sign
Dataset [4], German Traffic Sign Recognition Benchmark [5], German Traffic
Sign Detection Benchmark [6], Swedish traffic sign dataset [7], Chinese
Traffic Sign Database111http://www.nlpr.ia.ac.cn/pal/trafficdata/index.html
and Russian Traffic Sign Dataset (RTSD) [8].

One of the most recent traffic signs datasets is IceVisionSet [9]. It focuses
on Russian winter roads and covers both day and night conditions. Contrary to
many datasets, it provides sequences of raw Color Filter Array (CFA) frames
compressed using lossless algorithms. This dataset was used for the “Ice
Vision” competition, the final stage of which was held in July 2019.

In this paper we present a short description of the competition and the
solutions of the 3 top teams:

  * •

First place: Azat and Artem (Azat Davletshin, Artem Vasenin).

  * •

Second place: PsinaDriveNow (Alexey Harlamov, Pavel Solovev, Pavel Ostyakov).

  * •

Third place: Vizorlabs (Maksim S. Koriukin).

##  II The “Ice Vision” competition

Figure 3: Part of the “Ice Vision” leaders table available at
https://visiontest.misis.ru. Green numbers denote score for successful
detections and red numbers denote penalties for incorrect detections.

The competition was organized by the Russian Venture Company in association
with the National University of Science and Technology MISiS, Skolkovo
Institute of Science and Technology , and the “Starline” company. It was
divided into 2 stages: online and offline. The first stage was used to select
teams which will participate in the second one. Additionally, several foreign
teams have been invited to the offline stage, namely: the Harbin Institute of
Technology, the Politechnical University of Catalonia, the University of
Paris-Saclay, the University of Science and Technology Beijing, the University
of Wisconsin–Stout, and the University of Ulsan.

The competition was based on the IceVisionSet dataset. The annotations had two
distinct parts. The first part contained frame-by-frame annotations with
linearly interpolated bounding boxes created using Computer Vision Annotation
Tool (CVAT)222https://github.com/opencv/cvat, this part was presented in [9].
The second part was annotated after the paper submission. It was done in the
Supervisely web-tool, and had only approximately each 30th frame annotated (we
have used a random step between 25 and 35 frames, so participants will not
know which frames are annotated), but all annotations were manual.

Both dataset images and annotations are published under open CC BY 4.0 license
and can be downloaded from http://oscar.skoltech.ru and
https://github.com/icevision/annotations respectively. You can find annotation
statistics behind the second link.

For the competition an IoU-based metric was used. Bounding boxes with an area
smaller than 100 pixels were ignored during evaluation. The final result was a
sum of points for each correctly detected sign minus penalties for false-
positive detections (2 points for each such detection). If a sign was detected
twice, then the detection with the smallest IoU was counted as a false
positive. Scoring was handled using a scoring software with an open source
code, see: https://github.com/icevision/score.

During the online stage the participants had to detect only 10 sign classes.
See table in the README
v0.1.5333https://github.com/icevision/score/tree/v0.1.5. Detection was
considered successful if IoU was bigger or equal to 0.5 and the bounding box
had a correct class code. If IoU of a true positive detection was bigger than
0.85, it resulted in adding 1 point to final result. Otherwise the points were
calculated as $((IoU-0.5)/0.35)^{0.25}$.

During the offline stage the participants had to detect all signs defined by
the Russian traffic code. Detection was considered successful if IoU was
bigger or equal to 0.3 and the bounding box had a correct class or superclass
code. Score for true positives was calculated as $(1+k_{1}+k_{2}+k_{3})*s$,
where: $s$ is the “base” score, $k_{1}$ is a coefficient for detecting sign
code, $k_{2}$ is a coefficient for detecting associated data, $k_{3}$ is a
coefficient for detecting temporary sign. If $(1+k_{1}+k_{2}+k_{3})<0$, the
detection score was set to 0. If $IoU>0.85$, $s=1$. Otherwise it is calculated
using the following equation: $((IoU–0.3)/0.55)^{0.25}$. For a complete
description of coefficients computation please refer to the README.

Additionally, the participants were constrained by computational power and
time. During the offline stage they had to process 100 000 frames ( 50 000
stereo-pairs) in 5 hours using a virtual machine with a single NVIDIA Tesla
V100 and 8 core CPU, provided by Zhores Cluster444https://www.zhores.net/.

All participant’s submissions from the online and offline stages are published
under open CC BY 4.0 license and can be downloaded from:
https://github.com/icevision/solutions.

##  III The First Place Solution

###  III-A Algorithm overview

The team used a “detect and track” approach for their final solution. The
detector architecture was Faster R-CNN[10] with FPN[11], Cascade Head [12] and
Deformable Convolutions[13]. IoU-Tracker[14] was used for tracking. Each track
was further refined by combining class probabilities from all bounding boxes
belonging to a track and selecting the best one. For each track that was
classified as a speed limit sign, the team also predicted the speed limit
value.

The train set of the competition was relatively small, therefore the team pre-
trained the detector on COCO[15] and RTSD[8].

The code of the final solution can be found at:
https://gitlab.com/rizhiy/ice_vision.

###  III-B Detection

The team used MMdetection[16] as the base framework for their detection
pipeline. The baseline configuration was Faster R-CNN + FPN, pre-trained on
ImageNet[17]. Since the challenge had performance and time restrictions, the
team decided to use ResNet-50[18] as a backbone to balance speed and accuracy.

During the challenge, the team has performed several experiments to see which
approaches perform well on the competition dataset. For experiments evaluation
the standard COCO mAP metric was used.

As a starting point the team has used a model pre-trained on the COCO dataset
provided by the MMdetection framework. Next model was trained on RTSD, this
dataset also contains Russian traffic sign annotations for videos captured
from a moving car and is bigger than the training dataset used in the
competition. Finally the resulting model was fine-tuned on the training data
provided by organizers. The final training pipeline was as follows: COCO
$\rightarrow$ RTSD $\rightarrow$ IceVision.

The metric used in this competition awarded higher score to more precise
detections (higher IoU), therefore the team made an experiment using Cascade
Head. Cascade Head uses multiple stages to refine proposals produced by the
FPN, each trained with a higher IoU threshold. This approach produces more
precise detections.

While traffic signs have pretty defined shape, when captured from a car they
can appear squashed and twisted. To ease the amount of work the classifier has
to do, the team used Deformable convolutions which can bring features into a
unified shape.

Multi-scale training augmentation was used, as it almost always helps with
lack of data.

Many of the signs in the dataset were small (with an area less than 300
pixels). JPEG compression negatively impacts classification on small signs. By
using original raw Bayer PNM files provided by the organizers and using bi-
linear demosaicing to calculate RGB image expected by the backbone, the team
was able to improve classification on small signs.

The final configuration achieved COCO mmAP score of 0.412 on the validation
set.

###  III-C Tracking

Running detector on each frame at good resolution was not feasible because of
the computation power and timing constraints. Therefore the team has decided
to run the detector once every three frames, track the detections and linearly
interpolate the bounding boxes in-between. To track the bounding boxes the
team used IoU tracker.

IoU tracker works as follows: For each track, IoU is calculated for all
detections in a new frame. If the highest IoU is higher than the threshold,
that detection is added to the track. All detections which are not assigned to
a track, start as a new one.

During testing the team found that signs can move quite a lot even in three
frames, but are located sparsely. Therefore, lower IoU thresholds produced
better tracks. In final solution the team used IoU threshold of 0.1.

###  III-D Track Refinement

Usage of a tracker also allowed to improve the quality of detections. In the
video the same signs can be seen multiple times at various distances. By
tracking it, the team was able to use predictions from multiple frames.
Predictions were averaged and the best one was assigned to all detections in
the track.

Additionally, since competition allowed predicting super-categories, the team
added simple logic to combine class probabilities. The logic was as follows:
if none of the most specific class probabilities were above the threshold,
class probabilities of each $2^{nd}$-level-category were summed up. Each
probability was again checked if it was above the threshold and if not,
probabilities for top-level-categories would be summed up and again checked
against the threshold.

The team used different thresholds for each category level. Thresholds
themselves were found using grid-search on validation set.

Finally, if a sign was classified as a speed limit sign, the bounding box was
cut out from the original image and the number on the sign was predicted using
a ResNet-34.

Since the metric used in the competition summed, rather than averaged, scores
of each class, therefore the team did not attempt to fix class imbalance.

##  IV The Second Place Solution

The team used Cascade R-CNN [19] with the ResNeXt101 [20] backbone as a base
architecture. Training was done using the MMDetection framework [16] in 2
stages:

  1. 1.

Training of the full model (head and backbone) using RTSD dataset [8].

  2. 2.

Fine-tuning using IceVisionSet images after demosaicing, cropping of the lower
600 rows and resizing to 1632x966 pixels.

Due to the timing restrictions, ResNext101 features were computed only for
each 5th frame. Bounding box positions for in-between frames were interpolated
using cross-correlation metric.

Source code of the final solution can be downloaded from
https://github.com/gamers5a/SignDetection.

###  IV-A Interpolation

Matching of bounding boxes between keyframes was done using normalized cross-
correlation between sub-images selected by bounding boxes, i.e. without taking
classification results into account. After a pair of bounding boxes have been
found, a search area is found by adding/subtracting 20 pixels from
maximum/minimum coordinates of bounding boxes (see Fig. 5).

The size of an in-between bounding box was calculated using linear
interpolation, while position was found using maximum cross-correlation
response in the search area.

###  IV-B Signs classification

One of the tasks of the competition was the recognition of text on signs (city
signs, numerical values of the applicability of the signs. Since solving the
OCR problem by tailoring the conditions of entries provided by the organizers
requires a considerable amount of time, the team has decided to approach the
solution of the problem from the other side, namely, to solve the problem of
classifying text values rather than recognition.

One of the features was the inability to directly use flips during training,
since there are signs of pedestrian crossings in different directions in the
training, the rotation of which changed the annotation to the wrong one, which
is why during the training the team first used flips, and in the very last era
it was turned off for network to learn correct orientation of a sign.

###  IV-C Inference speed optimization

To accelerate the inference, the team used Float16, which is supported by the
MMDetection framework, while training was done using Float32. This approach
allowed to improve the network performance from 2 to 3 fps on the original
pictures. During dataset exploration, the team has noted that the camera is
installed on the machine in a such way that signs almost never appear in the
lower part of frames, which is expected since signs are installed to improve
visibility for drivers. Thus the team has ignored lower 600 out of 2048 rows,
it improved detector performance by additional $30\%$.

Since the final testing assumed a large load on the file system implemented
using IBM GPFS, the team has decided to reduce the use of random reading from
a remote file storage by asynchronously caching video sequences to RAM, in
parallel with their prediction, while the GPU and disk load were further
balanced using the Round-Robin algorithm. Since the the team solution required
3 independent passes through video sequences (a purely sequential reading of
which would take more time than was allocated for predicting the entire test
suite), this approach allowed the team to get rid of data loading problems.

###  IV-D Domain adaptation

The dataset contains both day and night sequences. Night sequences were under-
exposed in some parts to limit blur caused by the car movement. This is why
the team has used histogram equalization filter incorporated into the frame
conversion tool provided by the organizers. The histogram equalization filter
boost image contrast, but amplifies noise, degrades colors and changes
background brightness depending on the amount of bright objects in the frame.

This is why predictions on night data are less accurate than day data. To
improve the results on night sequences, the team has tried to increase dataset
diversity by applying Domain Adaptation of day sequences to night ones. To do
this, the team has trained MUNIT [21], to translate day frames to night ones
and vice versa. The reverse transformation turned out to be of rather poor
quality, because night images simply do not have all the necessary information
to restore the image, so only day to night conversions were added to the
training dataset.

Figure 4: Detection pipeline used by the PsinaDriveNow team. “B” stands for
“bounding box”, “C” for classification result and RPN for Region Proposal
Network. Figure 5: Interpolation pipeline used by the PsinaDriveNow team.

##  V The Third Place Solution

Most of the existing methods for object detection and classification can be
roughly divided into three main stages: proposal of a candidate object from
the region, selection of signs and classification. In order to establish the
regions that are likely to contain objects of interest, many methods use the
sliding window search strategy[22]. These methods use windows with different
scaling and ratio to scan the image with a fixed pitch size. An algorithm
called “Selective search” was proposed by Uuijlings et al.[23] to establish
possible locations of target objects. During the selective search, one starts
with each individual pixel as its own group. Then, one calculates the texture
for each group and combines the two textures that are the closest. The
algorithm continues to merge the regions until all groups are clustered.

These methods are widely used with deep convolutional networks for object
detection. In [24], the region proposal network (RPN) was offered, as well as
the new neural network architecture for objection detection named R-CNN. The
region proposal method (RPN) subsequently became the most popular method for
proposing the region for the object of interest. The R-CNN architecture
requires many region proposals to be precise, but many regions overlap with
each other. Instead of extracting the functions for each image patch from
scratch, the functions extractor based on the convolutional neural network can
be used [10]. The team has also used an external region proposal method
(selective search), to establish the areas of interest that are subsequently
combined with relevant object maps to obtain corrections for object detection.

The object detection methods which follow this approach are well known as two-
stage methods: proposal of the region candidate at the first stage and object
classification at the second one. The two-step methods based on convolutional
networks achieve high accuracy on object detection tasks, but have a very low
image processing speed. Methods which do not require additional operations
region proposals, such as YOLO [25] and SSD [26], represent so-called one-step
methods. They are much faster than the two-step methods, but have lower
accuracy. In particular, this trade-off makes them unsuitable for detection of
large number of small objects, which were common in the competition dataset.

###  V-A Implementation

Figure 6: Pipeline used by the VizorLabs team.

Large datasets is a key factor for training well-functioning CNN-based
architectures that have millions of parameters. In the past, some well-known
datasets for various tasks have been published, for example, ImageNet [27].

Due to the small amount of available training data, it was decided to enhance
it by augmenting it with synthetic data. The figure below shows the general
arrangement for the solution applied.

(a) Smaller than 30x30

(b) Bigger than 30x30

Figure 7: Spatial distribution of traffic signs in the IceVisionSet dataset
for different sign sizes.

The team used Cascade R-CNN as a neural network architecture with usage of the
transfer learning method [28] using the pre-trained ResNetXt-101 [20] on the
COCO dataset [15], and then set up the model for the IceVision dataset.
ResNetXt-101 CNN was used as a backbone. Adam [29] was used as an optimizer of
the objective error function, with a training rate of 5e-3. While preparing
the data for training, the team have used the Random Image Cropping approach.
Each batch of images included one fragment with the dimensions of 2448 × 1700.
The decision to use only part of the image for training was made upon viewing
the spatial distribution of traffic signs on all images (see Fig. 7). Objects
in a lower half of the frames were extremely rare.

##  VI Conclusions

In this work we have presented the “Ice Vision” competition focused on real-
time detection of Russian traffic signs in winter conditions. The IceVisionSet
used for the competition features lossless real world data collected on
Russian public roads in different conditions, including weather and
illumination. During the competition strong timing restrictions were enforced
upon participants.

This work covers 3 solutions of the competition winners. Two teams have
published the source code of their solutions.

We can observe that solutions share some similarities, common for modern deep
learning architectures, i.e. solutions use backbone/head architecture and
heavily rely on transfer learning, detectors work with single images without
utilizing temporal information outside of specialized algorithm-based
trackers. Probably due to the reliance on transfer learning solutions have not
used raw Bayer images and stereo information.

## References

  * [1] P. Paclik, “Road sign recognition survey,” _(Online)_ , 1999. [Online]. Available: http://euler.fd.cvut.cz/research/rs2/files/skoda-rs-survey.html
  * [2] H. Zhu, K.-V. Yuen, L. Mihaylova, and H. Leung, “Overview of environment perception for intelligent vehicles,” _IEEE Transactions on Intelligent Transportation Systems_ , vol. 18, no. 10, pp. 2584–2601, 2017.
  * [3] J. Janai, F. Güney, A. Behl, and A. Geiger, “Computer vision for autonomous vehicles: Problems, datasets and state-of-the-art,” _arXiv preprint arXiv:1704.05519_ , 2017.
  * [4] R. Timofte, K. Zimmermann, and L. Van Gool, “Multi-view traffic sign detection, recognition, and 3d localisation,” _Machine vision and applications_ , vol. 25, no. 3, pp. 633–647, 2014.
  * [5] J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition,” _Neural Networks_ , no. 0, pp. –, 2012. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0893608012000457
  * [6] S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing, and C. Igel, “Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark,” in _International Joint Conference on Neural Networks_ , no. 1288, 2013.
  * [7] F. Larsson and M. Felsberg, “Using fourier descriptors and spatial models for traffic sign recognition,” in _Scandinavian Conference on Image Analysis_. Springer, 2011, pp. 238–249.
  * [8] V. Shakhuro and A. Konushin, “Russian traffic sign images dataset,” _Computer Optics_ , vol. 40, no. 2, pp. 294–300, 2016.
  * [9] A. L. Pavlov, P. A. Karpyshev, G. V. Ovchinnikov, I. V. Oseledets, and D. Tsetserukou, “Icevisionset: lossless video dataset collected on russian winter roads with traffic sign annotations,” in _2019 International Conference on Robotics and Automation (ICRA)_. IEEE, 2019, pp. 9597–9602.
  * [10] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in _Advances in neural information processing systems_ , 2015, pp. 91–99.
  * [11] T. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie, “Feature pyramid networks for object detection,” _CoRR_ , vol. abs/1612.03144, 2016. [Online]. Available: http://arxiv.org/abs/1612.03144
  * [12] Z. Cai and N. Vasconcelos, “Cascade R-CNN: delving into high quality object detection,” _CoRR_ , vol. abs/1712.00726, 2017. [Online]. Available: http://arxiv.org/abs/1712.00726
  * [13] X. Zhu, H. Hu, S. Lin, and J. Dai, “Deformable convnets v2: More deformable, better results,” _CoRR_ , vol. abs/1811.11168, 2018. [Online]. Available: http://arxiv.org/abs/1811.11168
  * [14] E. Bochinski, V. Eiselein, and T. Sikora, “High-speed tracking-by-detection without using image information,” in _International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017_ , Lecce, Italy, Aug. 2017. [Online]. Available: http://elvera.nue.tu-berlin.de/files/1517Bochinski2017.pdf
  * [15] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in _European conference on computer vision_. Springer, 2014, pp. 740–755.
  * [16] K. Chen, J. Wang, J. Pang, Y. Cao, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Xu, _et al._ , “Mmdetection: Open mmlab detection toolbox and benchmark,” _arXiv preprint arXiv:1906.07155_ , 2019.
  * [17] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” _International Journal of Computer Vision (IJCV)_ , vol. 115, no. 3, pp. 211–252, 2015.
  * [18] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” _CoRR_ , vol. abs/1512.03385, 2015. [Online]. Available: http://arxiv.org/abs/1512.03385
  * [19] Z. Cai and N. Vasconcelos, “Cascade r-cnn: Delving into high quality object detection,” in _The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_ , June 2018.
  * [20] S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2017, pp. 1492–1500.
  * [21] X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz, “Multimodal unsupervised image-to-image translation,” in _ECCV_ , 2018.
  * [22] R. B. Girshick, P. F. Felzenszwalb, and D. A. Mcallester, “Object detection with grammar models,” in _Advances in Neural Information Processing Systems_ , 2011, pp. 442–450.
  * [23] J. R. Uijlings, K. E. Van De Sande, T. Gevers, and A. W. Smeulders, “Selective search for object recognition,” _International journal of computer vision_ , vol. 104, no. 2, pp. 154–171, 2013.
  * [24] R. Girshick, “Fast r-cnn,” in _Proceedings of the IEEE international conference on computer vision_ , 2015, pp. 1440–1448.
  * [25] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2016, pp. 779–788.
  * [26] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in _European conference on computer vision_. Springer, 2016, pp. 21–37.
  * [27] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in _2009 IEEE conference on computer vision and pattern recognition_. Ieee, 2009, pp. 248–255.
  * [28] S. J. Pan and Q. Yang, “A survey on transfer learning,” _IEEE Transactions on knowledge and data engineering_ , vol. 22, no. 10, pp. 1345–1359, 2009.
  * [29] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” _CoRR_ , vol. abs/1412.6980, 2014.