Real-Time Detection for Small UAVs: Combining YOLO and Multiframe Motion Analysis

Uncrewed 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 global–local YOLO-motion (GL-YOMO) detection algorithm, which combines you only look once (YOLO) object detection with multiframe 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 multiscale feature fusion and attention mechanisms, while the integration of the Ghost module further improves efficiency. In addition, 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 GL-YOMO algorithm significantly enhances detection accuracy and stability, underscoring its potential in UAV detection applications.