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.
Drones serve as critical tools for urban security surveillance and emergency response. However, aerial object detection faces inherent challenges, as key targets such as pedestrians and vehicles often appear tiny, densely distributed, and heavily occluded. These issues reduce detection accuracy and hinder real-world se...
To address the challenges of small object detection in UAV (Unmanned Aerial Vehicle) imagery, such as minute scales, complex backgrounds, and severe occlusions, this paper proposes an improved model based on YOLOv11n, designated as RMD-YOLO11n. First, the C3k2-RFAConv module is integrated into the backbone network, l...
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