The UAV Detection and Ranging Based on YOLOv4

The rapid development of UAV has brought great convenience to various application fields. In the meanwhile, its extensive utilization has also resulted in many problems such as public safety hazards, personal security threats and personal privacy violations. UAV is difficult to capture in real-time because of its small scale and complex flight environment. In order to solve the above problems from the perspective of security protection, a low-cost UAV detection, distance measure and protection scheme are proposed based on deep learning in this paper. The influences of different loss functions and thresholds are studied on the detection accuracy of YOLOv4 to improve the detection performance of YOLOv4 on UAV. At the same time, in order to achieve more effective prevention and control of UAV, the monocular ranging method based on PnP is introduced to get the distance between camera and UAV. Finally, the study is applied in the real-world scene, and a good target detection and ranging effect have been achieved so that the proposed model is verified in the feasibility and effectiveness.