Research on target recognition of underwater robot

In recent years, target recognition and detection technology, which is a very important research direction in the field of computer vision, is widely used in human life. The technology has been relatively mature for the recognition of targets such as people and objects on land. However, due to some conditions, it is relatively rare in the marine field. The main reason for the analysis is that underwater taxonomy and localization are affected by multiple factors such as illumination uniformity and obstruction, and underwater video acquisition is also relatively difficult. These issues have long been the focus of attention. Therefore, effective classification and recognition of targets in underwater video is of great significance for the intelligentization of marine equipment. This paper mainly locates and classifies the images of seacucumber, scallop and seaurchin. In this paper, the two methods of Faster-RCNN and YOLOv3 are used for experiments. The results show that the MAP and recall of YOLOv3 are higher than Faster-RCNN, and the recognition speed is relatively fast.