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

TL;DR

This paper introduces the GL-YOMO algorithm that combines YOLO and multi-frame motion analysis to improve real-time detection of small UAVs, addressing challenges of small target size and long-distance identification.

Contribution

The paper presents a novel global-local collaborative detection framework integrating optimized YOLO with motion analysis for small UAV detection.

Findings

Significant improvement in detection accuracy for small UAVs

Enhanced stability and efficiency in real-time detection

Validated on a custom UAV dataset with promising results

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 Global-Local 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. The YOLO detection algorithm is 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 GL-YOMO algorithm significantly enhances detection accuracy and stability, underscoring its potential in UAV detection applications.