Research on Collaborative Object Detection and Recognition of Autonomous Underwater Vehicle Based on YOLO Algorithm

Autonomous underwater vehicle (AUV) is an important tool for human to explore and research marine resources. Due to the influence of underwater environment, it is impossible to realize the rapid detection and recognition of AUV and the recognition of cooperative objects. To solve this problem, underwater light vision and underwater image enhancement technology are proposed to realize the rapid detection and recognition of underwater vehicles, so as to realize the recognition of cooperative objects. In this paper, YOLO algorithm which has a good effect on target detection precision and the recognition speed is used to recognize the underwater vehicle on the Yolo network. For the original data set, histogram equalization and CLAHE algorithm are used to enhance the image. They are respectively loaded into the same training model and trained on the yolov2 and yolov3 networks. The analysis of the experimental results shows that the underwater image enhancement technology based on the YOLOv3 network and the CLAHE algorithm can meet the requirements of rapid recognition for underwater vehicle detection and recognition.