UAV Chasing Based on YOLOv3 and Object Tracker for Counter UAV Systems

Abstract

This paper proposes a vision-based unmanned aerial vehicle (UAV) chasing system that can be embedded in a pursuer UAV (pUAV) to protect the attack from an evader UAV (eUAV). The proposed UAV chasing system consists of two parts, i.e., the UAV tracking and control signal generation parts. Combining a deep learning-based object detector, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">you only look once</i> version three (YOLOv3), and the existing object trackers, the proposed UAV tracking algorithm can improve the tracking performance of pUAV within affordable computational complexity. Then, the control signals of the pUAV are generated by utilizing the predicted bounding box area of the eUAV and the proportional-derivative control method. Following the object tracking benchmark performance criteria, various combinations of YOLOv3 and object trackers are examined and compared. From the evaluation results, the UAV tracking algorithm with the highest performance is chosen, whose average success rate and average precision rate for object tracking are 2.86% and 5.61% higher than YOLOv3, respectively. Through the field test, it is verified that the proposed UAV chasing system outperforms the YOLOv3 system in terms of bounding box misalignment (33% accuracy improvement) and computational complexity (71% reduction).