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 test our approach through experimental tests a water tank in the laboratory, an anechoic tank and the sea trial in the Huanghai sea in China. The results demonstrate the accurate performance of the proposed detection and positioning method.

The issue of underwater image restoration was investigated in this paper. Specifically, the color constancy of a single image was used to estimate the transmission map (TM), which can be used in the image formation model to restore the underwater image. First, the illumination component based on color constancy was used to estimate the refined TM without performing the guided filter or soft matting operation. Second, the statistical property of the pixel was used to fine-tune the color unbalance of underwater images. Finally, both qualitative and quantitative experimental results showed that the proposed method can not only obtain better restoration results, but also improve the real-time performance in different underwater scenes compared with other underwater image restoration methods.