Visual target detection for energy consumption optimization of unmanned surface vehicle
Liyong Ma, Xuewei Liu, Zhang Yong, Shuli Jia
Abstract
Unmanned surface vehicle (USV) is the future development direction of ships, but few studies have focused on USV’s energy optimization based on visual perception. An energy optimization strategy based on visual object detection is developed for USV. A visual target recognition method is proposed by combining YOLOv5 and DeepSORT. Visual recognition results are fused with radar targets to support route plan for energy optimization of USV. By dynamically adjusting the threshold of visual target recognition with the target number provided by radar, the target detection result is more accurate. Experimental results show that the proposed target detection method has the best performance than other commonly used methods, MOTA of the proposed method reaches 87.40%, and the YOLOv4 method, CenterTrack and FairMOT are 85.18%, 64.97% and 46.39% respectively. And the energy consumption optimization can be dynamically achieved by continuously analyzing the speed and path of the USV and predicting fuel consumption.