TL;DR
This work proposes LSO-YOLO, a novel real-time detector optimized for UAV-based small-object detection that significantly reduces parameter count and computational cost by approximately 67% and 18.18%, respectively, while maintaining a high inference speed of 126 FPS on an NVIDIA RTX 3060 GPU, meeting the requirements for real-time detection.
This work introduces a novel architecture termed LAI-YOLO, integrating attention mechanisms into a compact design that achieves 34.8% mAP50 on the VisDrone2019 dataset with only 2.38M parameters.
Object detection in unmanned aerial vehicle (UAV) swarm environments remains challenging due to extremely small target sizes, dense distributions, and frequent occlusions. AI-though deep learning-based detectors have progressed, conventional architectures suffer from the loss of fine-grained spatial information caused...
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.
The proposed WTM-YOLOv11 enhances the detection performance of small and occluded targets in drone aerial imagery, providing a robust and feasible solution for intelligent public security surveillance systems.
An improved model based on YOLOv11n, named RMD-YOLO11n, is proposed, which significantly improves multi-scale object detection performance in complex scenarios and effectively reduces the missed detection rate under complex backgrounds.
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