用YOLOv7和规则追踪法实现无人机视频检测，提升公共空间安防能力。
Drone Detection and Tracking with YOLO and a Rule-based Method
- 基于YOLOv7与改进变体，结合新扩展的公开数据集训练检测模型
- 通过交叉相关追踪减少单帧检测丢失，显著提升视频跟踪稳定性
- 适合关注无人机监管、智能安防系统开发的研究者与工程师
无人机（UAV）传统用于军事任务、作战及间谍活动，但随着安保、巡检、运输、科研及娱乐飞行等工业应用激增，其在公共空间的活动量大幅上升，亟需监管以保障隐私与安全。因此，非法入侵如越界飞行的检测成为必要。本文基于前期工作，扩展并分析了一个已发布的开源数据集，利用该数据集训练YOLOv7及其若干变体模型，并报告了实验结果。由于检测模型仅依赖单帧图像输入，采用基于交叉相关的简单追踪器以降低检测中断率，提升视频序列中的跟踪性能。最终整合形成完整的无人机检测与跟踪系统。
原文摘要 · Abstract (English)
Drones or unmanned aerial vehicles are traditionally used for military missions, warfare, and espionage. However, the usage of drones has significantly increased due to multiple industrial applications involving security and inspection, transportation, research purposes, and recreational drone flying. Such an increased volume of drone activity in public spaces requires regulatory actions for purposes of privacy protection and safety. Hence, detection of illegal drone activities such as boundary encroachment becomes a necessity. Such detection tasks are usually automated and performed by deep learning models which are trained on annotated image datasets. This paper builds on a previous work and extends an already published open source dataset. A description and analysis of the entire dataset is provided. The dataset is used to train the YOLOv7 deep learning model and some of its minor variants and the results are provided. Since the detection models are based on a single image input, a simple cross-correlation based tracker is used to reduce detection drops and improve tracking performance in videos. Finally, the entire drone detection system is summarized.
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