A object detection and tracking method for security in intelligence of unmanned surface vehicles

Unmanned surface vehicle is increasingly becoming a research hotspot, which can be used in a variety of civil and military missions. However, compared with the relative maturity of other technologies, the sensing technology of unmanned surface vehicles is relatively weak. Taking WAM-V-USV as the research platform, this paper is mainly focus on the detection and tracking methods of moving objects with unmanned surface vehicles. This paper introduces the environment sensing system of unmanned vehicle, water surface image preprocessing, water antenna detection based on SVM, and the method of water surface object detection and tracking based on improved YOLOV3. The simulation results show that the proposed method can effectively improve the accuracy of moving object detection and tracking. Through the practical application in the Songhua River and the US Unmanned Surface Vehicles Open, it is proved that the algorithm has a good detection and tracking effect and meets the real-time requirements. Practice has proved that the object detection and tracking method based on deep learning greatly improves the perception ability and self-security of the unmanned surface vehicles.

Automated summary

Unmanned surface vehicles are a research hotspot, but their sensing technology is relatively weak. This paper proposes an improved method for object detection and tracking, which effectively improves the accuracy of moving object detection and tracking.