Year: 2025
Type: article
Abstract: Unmanned Aerial Vehicle (UAV) detection technology plays a critical role in mitigating security risks and safeguarding privacy in both military and civilian applications. However, traditional detection methods face significant challenges in identifying UAV targets with extremely small pixels at long distances. To address this issue, we propose the GlobalLocal YOLO-Motion (GL-YOMO) detection algorithm, which combines You Only Look Once (YOLO) object detection with multi-frame motion detection techniques, markedly enhancing the accuracy and stability of small UAV target detection. Building upon the YOLOv5 architecture, the detection framework is further optimized through multi-scale feature fusion and attention mechanisms, while the integration of the Ghost module further improves efficiency. Additionally, a motion detection approach based on template matching is being developed to augment detection capabilities for minute UAV targets. The system utilizes a global-local collaborative detection strategy to achieve high precision and efficiency. Experimental results on a self-constructed fixed-wing UAV dataset demonstrate that the GLYOMO algorithm significantly enhances detection accuracy and stability, underscoring its potential in UAV detection applications.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411086345