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

Igor S. Golyak; Dmitriy R. Anfimov; Iliya S. Golyak; Andrey N. Morozov; Anastasiya S. Tabalina; Igor L. Fufurin

doi:10.1117/12.2573209

21 May 2020 Methods for real-time optical location and tracking of unmanned aerial vehicles using digital neural networks

Igor S. Golyak, Dmitriy R. Anfimov, Iliya S. Golyak, Andrey N. Morozov, Anastasiya S. Tabalina, Igor L. Fufurin

Author Affiliations +

Proceedings Volume 11394, Automatic Target Recognition XXX; 113941B (2020) https://doi.org/10.1117/12.2573209
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

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 are 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 %.

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Igor S. Golyak, Dmitriy R. Anfimov, Iliya S. Golyak, Andrey N. Morozov, Anastasiya S. Tabalina, and Igor L. Fufurin "Methods for real-time optical location and tracking of unmanned aerial vehicles using digital neural networks", Proc. SPIE 11394, Automatic Target Recognition XXX, 113941B (21 May 2020); https://doi.org/10.1117/12.2573209

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