Research on the anti-UAV distributed system for airports : YOLOv5-based auto-targeting device
Ruixi Liu, Yuxin Xiao, Zhidong Li, Hanlin Cao · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · 2022
The illegal intrusion of drones into airports poses a constant threat to public safety, and existing anti-drone technologies suffer from problems like blind detection zones and target loss. To address the shortcomings of existing methods, this paper proposes a distributed anti-drone system based on YOLOv5. Combined with the characteristics of airport defense UAVs intrusion scenarios, the system implements functions such as automatically targeting and releasing jamming signals to intercept illegal UAVs. In this paper, the YOLO algorithm is used to optimize the system’s detection of drones. The mechanical structure is used to achieve automatic targeting, effectively improving detection accuracy. The distributed cluster deployment is used to solve the defects of detection blind area and target loss. This paper provides a deployment idea for airport measures against lightweight UAV equipment through experimental validation, which provides theoretical guidance for future countermeasures against UAVs. The technology can be extended to railway stations and other infrastructures to ensure public safety jointly.