Research on automatic detection and tracking algorithm of UAV based on YOLOv4 and STC

Xinyan Wan; Yaoxiong Wang; Hao Li; Yuman Nie

doi:10.1117/12.2639734

19 October 2022 Research on automatic detection and tracking algorithm of UAV based on YOLOv4 and STC

Xinyan Wan, Yaoxiong Wang, Hao Li, Yuman Nie

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 1229448 (2022) https://doi.org/10.1117/12.2639734
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

Unmanned aerial vehicles (UAVs) are widely used in aerial reconnaissance, mapping, sports events, etc. However, due to insufficient control technology technologies, small UAV "illegal flight" incidents occur from time to time, posing a serious threat to the safe flight of civil aviation and normal air training of troops, and automated monitoring technology of UAVs is of great significance to air defense security. In this paper, the algorithm for UAV target detection and tracking is studied and improved for scenarios where UAVs are vulnerable to obstruction, change in their own scale, and are difficult to be captured in time when they enter the field of view from the edge. In this paper, a target detection model based on YOLOv4 is trained for UAV samples to achieve automatic response to UAVs could be recognized in the field of view, and it is also called every specified number of frames to update target detection information during the subsequent Spatio-Temporal Context (STC) tracking process. A cross-validation mechanism is completed by combining the YOLOv4 recognition and STC tracking results to achieve automatic and real-time surveillance of UAVs. The experimental results show that the loss value of the UAV target detection model is 0.3936, and the average recognition accuracy is 82.28%; This YOLOv4-STC fusion algorithm based on cross-validation has good recognition robustness in the case of target occlusion or intermittent reappearance compared with using YOLOv4 alone, and the recognition accuracy is improved compared with using STC alone. This algorithm also responds promptly and accurately to UAVs that enter from the edges of the field of view.

Citation Download Citation

Xinyan Wan, Yaoxiong Wang, Hao Li, and Yuman Nie "Research on automatic detection and tracking algorithm of UAV based on YOLOv4 and STC", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 1229448 (19 October 2022); https://doi.org/10.1117/12.2639734

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KEYWORDS

Target detection

Detection and tracking algorithms

Unmanned aerial vehicles

Automatic tracking

Target recognition

Defense technologies

Video

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