Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art (2309.04902v1)

Abstract: Transformers have rapidly gained popularity in computer vision, especially in the field of object recognition and detection. Upon examining the outcomes of state-of-the-art object detection methods, we noticed that transformers consistently outperformed well-established CNN-based detectors in almost every video or image dataset. While transformer-based approaches remain at the forefront of small object detection (SOD) techniques, this paper aims to explore the performance benefits offered by such extensive networks and identify potential reasons for their SOD superiority. Small objects have been identified as one of the most challenging object types in detection frameworks due to their low visibility. We aim to investigate potential strategies that could enhance transformers' performance in SOD. This survey presents a taxonomy of over 60 research studies on developed transformers for the task of SOD, spanning the years 2020 to 2023. These studies encompass a variety of detection applications, including small object detection in generic images, aerial images, medical images, active millimeter images, underwater images, and videos. We also compile and present a list of 12 large-scale datasets suitable for SOD that were overlooked in previous studies and compare the performance of the reviewed studies using popular metrics such as mean Average Precision (mAP), Frames Per Second (FPS), number of parameters, and more. Researchers can keep track of newer studies on our web page, which is available at \url{https://github.com/arekavandi/Transformer-SOD}.

• The paper demonstrates that vision transformers outperform CNNs in small object detection by effectively leveraging self-attention mechanisms.
• The paper identifies architectural optimizations and hybrid approaches as key strategies to enhance scale sensitivity in detection tasks.
• The paper highlights practical implications for real-time applications and suggests integrating domain-specific training to improve efficiency.

An Analysis of Vision Transformers for Small Object Detection

The paper "Vision Transformers for Small Object Detection: Do They Fit? And How to Get Better?" investigates the application of transformers in the field of small object detection (SOD) within computer vision. The authors aim to evaluate the performance of Vision Transformers (ViTs) against traditional Convolutional Neural Network (CNN)-based methods while identifying strategies to potentially enhance their capabilities in SOD tasks.

Transformers have gained prominence in various computer vision tasks due to their ability to outperform CNNs in several object detection scenarios. This paper explores the nuances of employing transformers specifically for SOD, where object scale poses unique challenges. Notably, the authors highlight that despite the high computational demands of transformers, their implementation leads to superior performance in SOD, prompting a deeper investigation into the factors contributing to this proficiency.

Key findings of the research are as follows:

• Transformers vs. CNNs: The paper illustrates that ViTs tend to outperform CNN-based approaches in SOD, which is consistent with their success in broader object recognition tasks. This observation is validated across a variety of image and video datasets, underscoring the transformers' suitability for SOD challenges.
• Performance Justifications: The authors argue that the architectural design of transformers, which emphasizes self-attention mechanisms, provides an inherent advantage in capturing long-range dependencies. This is particularly beneficial for accurately detecting small objects that may lack distinct features in CNN processing.

In the discussion section, the paper outlines potential strategies for further enhancing the SOD performance of transformers. The authors suggest that optimizing the scale-sensitive components of the transformer networks and experimenting with hybrid architectures that integrate both transformer and CNN elements could yield further improvements. Moreover, incorporating domain-specific knowledge into the training process is posited as a plausible avenue for better performance.

The paper concludes with a reflection on the implications of these findings, both from a practical standpoint, such as real-time applications in autonomous systems, and from a theoretical perspective, as it opens avenues for more SOD-centric architectural innovations. The authors acknowledge that while transformers currently hold an edge in this domain, the quest for efficiency and reduced computational overhead remains critical.

Future developments in AI could potentially see research evolving towards lightweight transformer models capable of operating in resource-constrained environments without compromising performance. Additionally, the integration of SOD-specific transformers into larger multi-modal systems presents an intriguing direction for further exploration.

In summary, this paper contributes to the understanding of transformers in small object detection and suggests avenues for future work to capitalize on their strengths, enabling more effective and efficient SOD solutions.