Tian Xiangrui, Jia Yinjun, Luo Xin, Yin Jie
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Sensors (Basel). 2022 Aug 31;22(17):6579. doi: 10.3390/s22176579.
Target recognition and tracking based on multi-rotor UAVs have the advantages of low cost and high flexibility. It can monitor low-altitude targets with high intensity. It has great application prospects in national defense, military, and civil fields. The existing algorithms for aerial small target recognition and tracking have the disadvantages of slow speed, low accuracy, poor robustness, and insufficient intelligence. Aiming at the problems of existing algorithms, this paper first makes a lightweight improvement for the YOLOv4 network recognition algorithm suitable for small target recognition and tests it on the VisDrone dataset. The accuracy of the improved algorithm is increased by 1.5% and the speed is increased by 3.3 times. Then, by analyzing the response value, the KCF tracking situation is judged, and the template update of the adaptive learning rate is realized. When the tracking fails, the target is re-searched and tracked based on the recognition results and the similarity judgment. Finally, experiments are carried out on the multi-rotor UAV, and the adaptive zoom tracking strategy is designed to track pedestrians, cars, and UAVs. The results show that the proposed algorithm can achieve stable tracking of long-distance small targets.
基于多旋翼无人机的目标识别与跟踪具有成本低、灵活性高的优点。它能够高强度地监测低空目标。在国防、军事和民用领域具有广阔的应用前景。现有的空中小目标识别与跟踪算法存在速度慢、精度低、鲁棒性差以及智能化不足等缺点。针对现有算法的问题，本文首先对适用于小目标识别的YOLOv4网络识别算法进行轻量化改进，并在VisDrone数据集上进行测试。改进算法的准确率提高了1.5%，速度提高了3.3倍。然后，通过分析响应值判断KCF跟踪情况，实现自适应学习率的模板更新。当跟踪失败时，基于识别结果和相似度判断对目标进行重新搜索和跟踪。最后，在多旋翼无人机上进行实验，设计自适应变焦跟踪策略来跟踪行人、汽车和无人机。结果表明，所提算法能够实现对远距离小目标的稳定跟踪。
