TL;DR
Compared to existing state-of-the-art methods, LightS-DETR delivers superior detection accuracy and robust real-time performance while significantly reducing computational demands, offering an optimal balance for UAV deployment.
Small-object detection in unmanned aerial vehicle (UAV) imagery is challenging because targets are often tiny, densely distributed, and embedded in cluttered backgrounds, while edge platforms impose strict computational constraints. This paper presents a lightweight end-to-end detector based on RT-DETR to improve detec...
A gated feature fusion net is designed to reduce model parameters and a small object detection pyramid is implemented to preserve high-resolution details and integrate a mini-kernel that utilizes a lossless down-sampling module for deep feature optimization.
A lightweight, high-precision framework extending the YOLOv11 architecture, integrating Progressive Channel-wise Self-Attention and Dynamic Tanh, which provides a practical and efficient solution for real-time aerial surveillance at night.
HD-YOLO improves small-object detection with a compact parameter footprint, while direct hardware benchmarks remain necessary to establish deployment efficiency.
Improved PF-DETR improves the model’s robustness and accuracy in detecting multi-scale and small targets in complex and cluttered scenes, resulting in a favorable balance between detection performance and model efficiency.
Results highlight the effectiveness of SOD-MSC for dense small object detection in complex traffic environments, with promising applications in congestion monitoring, vehicle counting, and related ITS tasks.
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