融合多帧运动分析与YOLO，提升远距离小型无人机检测精度。
Real-Time Detection for Small UAVs: Combining YOLO and Multi-frame Motion Analysis
- 结合多尺度特征融合与注意力机制优化YOLO，提升小目标识别能力。
- 引入基于模板匹配的多帧运动分析，检测精度提升23.6%。
- 适合安防、边境监控等需实时识别微小飞行物的场景。
无人飞行器（UAV）检测技术在军事和民用领域对降低安全风险、保护隐私至关重要。然而，传统方法难以在远距离下识别像素极小的UAV目标。为此，本文提出全局-局部联合的YOLO-运动检测算法（GL-YOMO），将YOLO目标检测与多帧运动分析相结合，显著提升小规模UAV目标的检测准确率与稳定性。通过多尺度特征融合与注意力机制优化YOLO，同时引入Ghost模块提升计算效率；并开发基于模板匹配的运动检测方法，增强对微小目标的捕捉能力。系统采用全局-局部协同检测策略，在自建固定翼无人机数据集上的实验表明，该算法在远距离小目标检测中表现优异，验证了其在实际应用中的潜力。
原文摘要 · Abstract (English)
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.
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