Vision-Based Target Detection and Positioning Approach for Underwater Robots

The accurate target detection under different environmental conditions and the real-time target positioning are vital for the successful accomplishment of underwater missions of Remotely operated vehicles (ROVs). In this paper, we propose a vision-based underwater target detection and positioning approach to detect and estimate the position and attitude of artificial underwater targets. The proposed approach is composed of an underwater target detection algorithm YOLO-T and a target positioning algorithm. Firstly, we modify the structure of YOLOv5 algorithm using Ghost module and SE attention module to improve the calculation time of target detection. Secondly, a series of image processing operations are performed on the improved YOLOv5 detection results to increase the detection accuracy. Thirdly, a cooperative marker is designed as the artificial underwater target, and the corresponding positioning algorithm is presented to calculated the position and attitude of the target according to the geometric information of the designed marker. We validate our approach through experimental tests respectively in a water tank, an anechoic tank, and the sea trial in Huanghai Sea in China. The results demonstrate the accurate performance of the proposed detection and positioning method.