In recent years, unmanned aerial vehicle (UAV) technology has developed rapidly, which plays an important role in both military and civil fields. While it brings convenience to all walks of life, there are also a lot of security problems. Therefore, it is necessary to study anti-UAV technology, but the detection of UAV is an important basis of anti-UAV technology. In this paper, by combining the three-dimensional range profile collected by Gm-APD (Geiger mode Avalanche Photo Diode) lidar with the deep learning method, a UAV detection method based on the improved YOLOv3 (You Only Look Once v3) network is proposed. Firstly, the data of UAV in low altitude real scene is collected by Gm-APD lidar, and range profile information is generated. Then, the YOLOv3 network is improved, and the SPP module can be added to combine the local features of the target with the global features to improve the detection accuracy. It is suitable for the detection of UAVs. Finally, the trajectory of the UAV is drawn based on the distance information. The experimental results show that the improved method can effectively detect UAV targets, and is better than the previous method, YOLOv3-Tiny method and YOLOv4 method. The farthest detection distance is 238m, the detection speed is fast, and the purpose of real-time detection and positioning can be achieved. Therefore, this method can be applied to anti-UAV technology and has important research significance.
Research on UAV Detection Technology of Gm-APD Lidar Based on YOLO Model
2021-10-15
819718 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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