# SOAR: Advancements in Small Body Object Detection for Aerial Imagery Using
State Space Models and Programmable Gradients

Tushar Verma  Netaji Subhas University of Technology
0009-0006-0299-9702  Yash Bhartari  Netaji Subhas University of Technology
0009-0008-9465-5572  Rishi Jarwal  Netaji Subhas University of Technology
0009-0008-4179-5045  Suraj Singh  Netaji Subhas University of Technology
0009-0008-8696-0876  Shubhkarman Singh  Netaji Subhas University of Technology
0009-0000-5602-5432  Jyotsna Singh  Netaji Subhas University of Technology

###### Abstract

Small object detection in aerial imagery presents significant challenges in
computer vision due to the minimal data inherent in small-sized objects and
their propensity to be obscured by larger objects and background noise.
Traditional methods using transformer-based models often face limitations
stemming from the lack of specialized databases, which adversely affect their
performance with objects of varying orientations and scales. This underscores
the need for more adaptable, lightweight models. In response, this paper
introduces two innovative approaches that significantly enhance detection and
segmentation capabilities for small aerial objects. Firstly, we explore the
use of the SAHI framework on the newly introduced lightweight YOLO v9
architecture, which utilizes Programmable Gradient Information (PGI) to reduce
the substantial information loss typically encountered in sequential feature
extraction processes. The paper employs the Vision Mamba model, which
incorporates position embeddings to facilitate precise location-aware visual
understanding, combined with a novel bidirectional State Space Model (SSM) for
effective visual context modeling. This State Space Model adeptly harnesses
the linear complexity of CNNs and the global receptive field of Transformers,
making it particularly effective in remote sensing image classification. Our
experimental results demonstrate substantial improvements in detection
accuracy and processing efficiency, validating the applicability of these
approaches for real-time small object detection across diverse aerial
scenarios. This paper also discusses how these methodologies could serve as
foundational models for future advancements in aerial object recognition
technologies. The source code will be made accessible here
https://github.com/yash2629/S.O.A.R

###### Index Terms:

Small-Object Detection, Vision Transformers, State Space Models, Remote
Sensing, YOLO, Image Processing, Mamba, Supervised Learning, Neural Networks

##  I Introduction

A fundamental component of many applications, ranging from video surveillance
to intelligent traffic management and digital city infrastructure, is the
detection and tracking of objects in diverse visual media, object detection
identifies a target object inside a single frame or image. These qualities are
critical for many computer vision applications. The biggest challenge seems to
be Small Object detection. An object is considered little if it appears tiny
in the video frame, usually because it was monitored from a distance. Their
tiny size sometimes leads to them being confused for noise, which has a
detrimental effect on tracking accuracy. The small objects can be defined as
MS-COCO [1] metric evaluation, small objects are defined as having an area of
32 × 32 pixels or less, which is a commonly used threshold for datasets
including common objects. There have been some models that have been developed
to help in the particular problem and continuous improvement is being done on
them too.

While the results from currently available oriented aerial detectors are
encouraging, they primarily concentrate on orientation modeling and pay little
attention to object size. Identifying the area contained by bounding boxes
using object detection is a useful method for comprehending things in an image
by explaining what these objects are and where they are. Using a rectangular
bounding box without an angle orientation also known as a horizontal bounding
box (HBB) is the standard procedure. The model must be able to locate an
object with accuracy and recognize its class to contain it inside an HBB.

However, this method is particularly unsuccessful at detecting oriented aerial
objects; more noise and background will be enclosed, which can cause
misdetection; objects cannot be properly localized. Consequently, an
orientated bounding box (OBB) producing object detector was introduced.
Techniques already in use aided in the creation of efficient OBB detectors
that can precisely encompass orientated objects. Refinement features [2, 3,
4], proposal extraction [5, 6, 7, 8], orientation alignment [7, 9], and
regression loss design [10, 11, 12] are some of the approaches that fall under
this category.

Despite the improved performance, the work on small object detection continued
because of the combination of multi-scale prediction, shallow and deep
networks, and more loss functions for large-scale object recognition while
small objects were disregarded. Keeping all that in mind work on some new
models with multi-scale feature fusion was encouraged. Adapting towards the
same path this model takes inspiration from these developments and has to
concentrate more on the expression of physical information about small items
through multi-scale detection to achieve accurate small object detection. To
anticipate small objects, the algorithm will be needed to combine deeper
backbone networks and additional scales. The concept of a feature pyramid can
be applied to more sensibly express the physical information of the shallow
network and the semantic properties of the deep network. But previously it has
come to notice that this can lead to an issue such as the backbone network
getting deeper, the network parameter growing as well, and the quantity of the
computation. Further efforts can surely be made to enhance the backbone
network and achieve faster detection rates while maintaining a high level of
detection accuracy.

While the use of deep learning architectures has led to the development of
very accurate techniques like RetinaNet [13], VarifocalNet [14], Cascade R-CNN
[15], and Faster R-CNN [16], these techniques are not without their
variations. All of these new detectors are tested and trained on popular
datasets such as MS COCO [1], Pascal VOC12 [17], and ImageNet [18]. These
datasets mostly consist of low-resolution photos (640 x 480) that feature huge
objects with extensive pixel coverage, typically encompassing 60% of the image
height. Long-range object detection that satisfies the Detection, Observation,
Recognition, and Identification (DORI) [19] requirements has been made
possible by those developments. It was suggested to slice to maintain higher
memory utilization while aiding inference and fine-tuning for small item
detection on high-resolution photos. To create the detection, use anchor boxes
and categorize every point on the feature pyramid [20] as either background or
foreground. Then forecast the distances between the foreground point and the
four corners of the ground-truth bounding box directly.

Talking about more on Small Object detection the YOLO series is the most
popular real-time object detector, it is currently the standard in real-time
object detection. YOLOv7 has been utilized in the past for small object
recognition and has shown to be successful in a range of computer vision tasks
and settings. The newly introduced YOLOv9 is believed to be the best real-time
object detector of the new generation due to the aforementioned innovative
technique. This, when combined with SAHI, can significantly enhance the new
model’s capability over its predecessors.

It has been discovered that the generic slicing supports an inference pipeline
that can be applied on top of any object detector in use thus helping with its
fine-tuning. In this manner, slicing-assisted inference improved the small
object detection performance of any object detector that is currently on the
market without the need for fine-tuning and there is no need for pretraining
when integrating the slicing-assisted hyper inference scheme into any object
detection inference pipeline. Furthermore, optimizing the pre-trained models
resulted in an extra performance benefit.

Review of Contributions:

  * •

YOLOv9, a novel aerial imagery platform, is deployed on DOTA. It improves upon
prior works by combining sliced-aided hyper inferencing pipeline adapters with
pretraining regimes.

  * •

A new framework for dynamic small-body object detection and experimental
validation using SOAR over Vision Mamba architecture.

  * •

Proposal of a novel framework towards fusion of Programmable gradient
information with state space model representation for effective visual and
computer vision task settings.

##  II Related Works

In recent years, object detection research has witnessed significant
advancements propelled by innovative methodologies and algorithms tailored to
address specific challenges. This section presents a review of relevant
literature focusing on enhancing object detection performance, particularly in
scenarios involving small or distant objects.

The region-based convolutional neural network (RCNN) was one of the first
successful deep-learning approaches to detect objects. The performance was
achieved through two insights. The first was to apply high-capacity
convolutional neural networks to bottom-up region proposals to localize and
segment objects. The second was a paradigm for training large CNNs when
labeled training data is scarce. Also, earlier work combined handcrafted
characteristics with deep learning-based features to enhance object detection,
from the aegis within the YOLOv3 architecture. By leveraging hierarchical
representations of Convolutional Neural Networks (CNNs) and domain-specific
information stored in handmade features, the method overcomes the limitations
of previous techniques. Through careful selection of convolutional layers for
feature injection and refining feature combinations, substantial improvements
in mean Average Precision (mAP) are observed compared to YOLOv3 on benchmark
datasets such as PASCAL-VOC and MS-COCO. The study emphasizes the potential of
feature fusion techniques in object detection, demonstrating considerable
gains in detection robustness and accuracy.

Another notable contribution comes from the MSFYOLO algorithm. This algorithm
enhances small object detection by integrating a side path for feature re-
fusion within the FPN [20] architecture, based on the PANET [21] framework.
Through thoughtful loss function design and extensive testing, MSFYOLO
outperforms existing methods such as YOLOv5 and RetinaNet, especially in
challenging conditions, exhibiting robustness and high Frames Per Second
(FPS)[20]. The algorithm’s effectiveness positions it for real-world
applications where both efficiency and accuracy are critical, spanning fields
like industrial inspection, autonomous driving, surveillance, and medical
imaging.

Addressing the challenges of detecting small and distant objects in
surveillance imagery, the SAHI framework introduces a slicing-aided hyper-
inference approach. This method seamlessly integrates into existing object
detection systems without pretraining, enhancing the detectability of small
objects by slicing input images into overlapping patches during both fine-
tuning and inference. Experimental evaluations demonstrate significant Average
Precision (AP) improvements, particularly for small objects, across multiple
datasets and detectors. SAHI offers a practical solution for small object
detection in surveillance applications, with potential implications across
various domains.

Lastly, the study on Multiscale Faster-RCNN [16] addresses challenges in small
object detection in machine vision. Leveraging multiscale feature extraction,
the proposed approach demonstrates superior small object detection compared to
Faster-RCNN [16], validated through experimental evaluation and real-world
scenarios. The study underscores the significance of tackling small object
detection challenges and suggests avenues for future exploration, such as
employing advanced techniques like Generative Adversarial Networks (GANs).

Collectively, these studies represent significant strides in the field of
object detection, offering diverse methodologies and algorithms tailored to
enhance detection performance, particularly in scenarios involving small or
distant objects.

Figure 1: The architecture of the SOAR framework

##  III Methodology

###  III-A Preliminaries

The State Space Model (SSM) is designed to characterize one-dimensional
functions or sequences, where $u(t)\in\mathbb{R}$ maps to $v(t)\in\mathbb{R}$
via a concealed state $z(t)\in\mathbb{R}^{N}$. Employing $E$ as the evolution
parameter and $F$ and $G$ as the projection parameters, the system operates
with the following equations:

$z(t)=Ez(t)+Fu(t),\\\ \quad v(t)=Gz(t)$ (1)

To discretize the continuous parameters, a timescale parameter $\Delta$ is
employed, a technique commonly referred to as zero-order hold, expressed as
follows:

$\overline{E}=\exp(\Delta E),\quad\overline{F}=(\Delta E)^{-1}(\exp(\Delta
E)-I)\cdot\Delta F$ (2)

The discretized version of the original equations is represented as:

$z_{t}=Ez_{t-1}+Fu_{t},\quad v_{t}=Gz_{t}$ (3)

A step of $\Delta$ is utilized in these equations.

Finally, the model calculates the output via global convolution:

$K=(G\overline{F},G\overline{EF},\ldots,G\overline{E^{M-1}F}),\\\ \quad
v=u*K,$ (4)

where $M$ signifies the length of the sequence and $K\in\mathbb{R}^{M}$
denotes a structured convolution kernel.

###  III-B Programmable Gradients Information

The YOLOv8 model faced problems with small objects in images. Minimal pixel
objects were difficult to accurately represent because the model’s receptive
field might not be able to capture enough information. According to the
principle of information bottleneck, the data causes information loss when
going through transformations as shown in Equation (5).

$I(X,X)\geq I(X,f(X))\geq I(X,g_{\phi}(f_{\theta}(X)))$ (5)

Where $X$ indicates the data, $I$ indicates mutual information, $f$ and $g$
are transformation equations, and $\theta$ and $\phi$ are parameters of $f$
and $g$ respectively.

The following information is used to tell that as the number of network layers
increases there will be a loss of original data. To decrease this information
loss, reversible functions are used as shown in Equation (6):

$I(X,X)=I(X,r_{\psi}(X))=I(X,v_{\zeta}(r_{\psi}(X)))$ (6)

where $\psi$ and $\zeta$ are parameters of $r$ and $v$, and for the
application of the above method on lightweight models, the concept of
information bottleneck is used. The formula for the same is:

$I(X,X)\geq I(Y,X)\geq I(Y,f_{\theta}(X))\geq\ldots\geq I(Y,\hat{Y})$ (7)

Where $I(Y,X)$ will only occupy a very small part of $I(X,X)$. To counter all
the aforementioned problems, a programmable gradient information method is
used, which uses an auxiliary reversible branch and multi-level auxiliary
information.

The YOLOv9 [22] model also uses a Generalised ELAN network made from combining
ELAN [23] and CSPNet [24]. For improved learning of small objects in the
usually large datasets, hyper-inferencing is used. It is applied on top of the
YOLOv9 model for object detection, providing a generic slicing-aided inference
and fine-tuning pipeline. Sliced Aided Hyper Inferencing [25] divides the
images into overlapping patches which result in a larger pixel area for small
objects. It has a 2-part process combining slice-aided fine-tuning and slice-
aided hyperinferencing. During the fine-tuning process, the data is enlarged
by extracting patches and resizing them into larger images. Each image
$I_{F1},I_{F2},\ldots,I_{Fj}$ is sliced into overlapping patches
$P_{F1},P_{F2},\ldots,P_{Fk}$ with dimensions $M$ and $N$ selected within
predefined ranges $[M_{\text{min}},M_{\text{max}}]$ and
$[N_{\text{min}},N_{\text{max}}]$, which are treated as hyper-parameters. Then
the patches are resized taking care of the aspect ratio, which results in
larger object sizes as compared to that of the original image, while during
the process of inferencing, the image is divided into smaller patches and then
object detection forward pass is applied to each of the independent patches.
Finally, the overlapping prediction results are merged into the original
image.

###  III-C Overall architecture

An overview of the proposed SOAR encoder is shown in Fig.1. To streamline the
processing of visual data, we start by transforming the 2D image
$I\in\mathbb{R}^{H\times W\times C}$ into flattened 2D patches
$P\in\mathbb{R}^{J\times(S^{2}\cdot C)}$, where $(H,W)$ denotes the dimensions
of the input image, $C$ represents the number of channels, and $S$ denotes the
size of the image patches. Subsequently, a linear projection of $P$ onto a
vector with dimensionality $D$ is conducted, integrating position embeddings
$E_{\text{pos}}\in\mathbb{R}^{(J+1)\times D}$ using the equation:

$V_{0}=[V_{\text{cls}};V^{1}_{P}\cdot Z;V^{2}_{P}\cdot Z;\ldots;V^{J}_{P}\cdot
Z]+E_{\text{pos}}$ (8)

Here, $V_{\text{cls}}$ denotes the class token, $V^{j}_{P}$ signifies the
$j$-th patch of $I$, $Z\in\mathbb{R}^{(S^{2}\cdot C)\times D}$ represents the
learnable projection matrix, and $J$ indicates the total number of patches.
This procedure facilitates the preparation of image data for subsequent
analysis, embedding contextual information through position embeddings.
Inspired by Vision Mamba [26] and ViT [27], we then forward the token sequence
$V_{l-1}$ to the $l$-th layer of the SOAR encoder to produce $V_{l}^{o}$. In
the SOAR encoder, the input token sequence $V_{l-1}$ undergoes normalization
by the normalization layer. Subsequently, the normalized sequence is linearly
projected to $x$ and $z$ with a dimension size $E$. Following this, $x$ is
processed in both forward and backward directions as shown in (1). For each
direction, a 1-D convolution is applied to $x$, yielding $x^{\prime}_{o}$.
Subsequently, $x^{\prime}_{o}$ is linearly projected to $F_{o}$, $G_{o}$, and
$\Delta_{o}$ as in (2). This $\Delta_{o}$ is subsequently used to obtain
$F_{o}$, $E_{o}$ post which $V_{\text{forward}}$ and $V_{\text{backward}}$ are
processed by the State Space Model.

$Q=\text{Norm}(V_{L}^{o})$ (9) $T=\text{MLP}(Q)$ (10)

The introduction of a programmable gradient information layer fused on top of
the decoder block consisting of an MLP head is further proposed and exploited
as a promising future work toward the fusion of these novel architectures.

##  IV Experimentation and Results

In this section, we evaluate our neuro-graphic retrieval agent framework
through quantitative and qualitative experiments, aiming to assess its
performance and versatility across diverse and complex tasks.

###  IV-A Experimental Setup

We implemented our approach in PyTorch. We used 2 machines with a 24GB GPU
server each. We used the Distributed Data Parallelism (DDP) technique for
multi-machine training since the model was too large to fit on a single GPU
which was available to us.

###  IV-B Datatset

The datasets used for small object detection and tracking are essential for
evaluating and benchmarking the performance of various algorithms in this
field and the research conducted herein relies on the comprehensive and
diverse dataset provided by DOTA [28] specifically DOTAv1.5. DOTA is a sizable
dataset used for aerial image object detection. It can be applied to the
development and assessment of aerial picture object detectors. The photos are
gathered from various platforms and sensors. Every image has a pixel size
ranging from 800 x 800 to 20,000 x 20,000 pixels. The China Centre for
Resources Satellite Data and Application, Google Earth, GF-2 and JL-1
satellites, and aerial photos from CycloMedia B.V. are the sources of the
DOTA-v1.5 photographs. DOTA is made up of both grayscale and RGB images. The
grayscale images are from the panchromatic band of GF-2 and JL-1 satellite
photographs, while the RGB images are from Google Earth and CycloMedia. Every
image is kept in a ’png’ format. Planes, ships, storage tanks, baseball
diamonds, tennis courts, basketball courts, ground track fields, harbors,
bridges, big vehicles, small vehicles, helicopters, roundabouts, soccer ball
fields, swimming pools, and container cranes are the objects types in
DOTA-v1.5. There were a total of 1869 images in total in the dataset which
included 1410 train sets, 438 valid sets, and 21 test sets. The preprocessing
Grayscale was applied. We converted labels from dota format to coco format
using dota dev kit.

###  IV-C Implementation Details

In our paper, we employ a fixed input image size of 224 × 224 and implement
data augmentation techniques including random cropping, flipping, photometric
distortion, mixup, cutMix, etc. Images are processed into sequential data
through a two-dimensional convolution with a kernel size of 16 (k = 16) and a
stride of 8 (s = 8). Position encodings are represented by randomly
initialized learnable parameters. For supervised training, we employ the
cross-entropy loss function and utilize the AdamW optimizer with an initial
learning rate of 5e 4 and a weight decay of 0.05. The learning rate is decayed
using a cosine annealing scheduler with a linear warmup. The batch size for
training is set at 16,32 & 64, and the training process spans a total of 200
epochs Fig.4. We employ Precision (P), Recall (R), and F1-score (F1) as
performance metrics as shown in Fig.2

Figure 2: F1, Precision, Recall, and PR curves

###  IV-D Results and Discussion

TABLE I: Computation Comparison of SOAR with CNN and Transformer based models MODEL |  PARAMS(M) |  GFLOPS
---|---|---
SNUNet |  10.21 |  176.36
TransUNetCD |  28.37 |  244.54
SwinSUNet |  39.28 |  43.50
SOAR |  17.13 |  45.74

This study has made substantial progress in small object detection in aerial
imagery by employing the SAHI framework on YOLO v9 and the Vision Mamba model
with a bidirectional State Space Model. These innovations have effectively
tackled the challenges of detecting small objects obscured by background
noise, enhancing detection accuracy and computational efficiency. The
integration of Programmable Gradient Information (PGI) and position embeddings
allows for a nuanced, location-aware analysis that is well-suited for remote
sensing and computer vision task settings. These findings demonstrate the
potential of these lightweight and adaptable models to serve as foundational
technologies for future advancements in aerial object recognition and other
complex visual tasks. As we continue to refine these methods, they promise to
significantly impact the development of computer vision, particularly in
resource-limited scenarios.

TABLE II: Accuracy Assessment of SOAR with CNN and Transformer Based Models MODEL | Rec | Pre | OA | F1 | IoU | KC
---|---|---|---|---|---|---
SNUNet | 72.21 | 74.09 | 87.49 | 73.14 | 57.66 | 64.99
TransUNetCD | 77.73 | 82.59 | 90.88 | 80.09 | 66.79 | 74.18
SwinSUNet | 79.75 | 83.50 | 91.51 | 81.58 | 68.89 | 76.06
SOAR | 79.59 | 83.06 | 91.36 | 81.29 | 68.48 | 75.68
Figure 3: Inferencing Results

##  V Conclusion

This study has made substantial progress in small object detection in aerial
imagery by employing the SAHI framework on YOLO v9 and the Vision Mamba model
with a bidirectional State Space Model. These innovations have effectively
tackled the challenges of detecting small objects obscured by background
noise, enhancing detection accuracy and computational efficiency. The
integration of Programmable Gradient Information (PGI) and position embeddings
allows for a nuanced, location-aware analysis that is well-suited for remote
sensing and computer vision task settings. These findings demonstrate the
potential of these lightweight and adaptable models to serve as foundational
technologies for future advancements in aerial object recognition and other
complex visual tasks. As we continue to refine these methods, they promise to
significantly impact the development of computer vision, particularly in
resource-limited scenarios.

(a) Confusion Matrix

(b) Train v/s Loss curves

Figure 4: Results and Analysis

##  VI Acknowledgements

The authors express their gratitude to their fellow peers and the High
Performace Computing & Artificial Intelligence Centre at Netaji Subh‘as
University of Technology for the resources and invaluable feedback during the
course of this project.
During the preparation of this work, the authors utilized ChatGPT [29], to
assist with generating initial data analysis insights and refining the
sections of the manuscript for literature review. After utilizing this tool,
the authors carefully reviewed and edited the content as needed and take full
responsibility for the content of the publication

Figure 5: Inferencing Results from another batch

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