Methods for real-time optical location and tracking of unmanned aerial vehicles using digital neural networks

摘要

Unmanned aerial vehicles (UAVs) play important role in human life. Today there is a high rate of technology development in the field of unmanned aerial vehicles production. Along with the growing popularity of the private UAVs, the threat of using drones for terrorist attacks and other illegal purposes is also significantly increasing. In this case the UAVs detection and tracking in city conditions arc very important. In this paper we consider the possibility of detecting drones from a video image. The work compares the effectiveness of fast neural networks YOLO v.3, YOLO V.3-SPP and YOLO v.4. The experimental tests showed the effectiveness of using the YOLO v.4 neural network for real-time UAVs detection without significant quality losses. To estimate the detection range, a calculation of the projection target points in different ranges was performed. The experimental tests showed possibility to detect UAVs size of 0.3 m at a distance about 1 km with Precision more than 90 %.