Abstract: Small unmanned aerial vehicles (UAVs) show great potential in multiple fields, but they may lead to abusive behaviors such as illegal mapping, reconnaissance, and interference with aviation order. Therefore, effective detection and tracking strategies are urgently needed. Traditional radars have limitations in tracking small UAVs in complex urban environments, while vision-based deep learning methods, although with high accuracy, have large computational overhead. To address the above challenges, this paper proposes a detection and tracking algorithm based on lightweight YOLOv3-tiny and interactive multiple model Kalman filter (IMM-KF). YOLOv3-tiny is used for low-frequency detection, and IMM-KF realizes tracking through high-frequency prediction and state updates of multiple motion models, which effectively reduces the computational power requirements and can deal with the problem of target loss when the target is occluded. Experimental results show that the detection and tracking accuracy of this algorithm in complex urban environments reaches 98.33%, with a real-time coverage rate of 73.6%, which significantly improves tracking efficiency and stability and meets the needs of UAV supervision.

Key words: interactive multiple model Kalman filter, visual tracking, YOLO algorithm, XTDrone environment