Automatic Detection Algorithm of Mine Detection Based on Improved YOLOv5 in Complex Underwater Environment for AUV
Yuxin Zhao, Xue Shang, Enjiao Zhao, Xiong Deng, Zhengyang Wang, He Jianxin · 2022 5th International Symposium on Autonomous Systems (ISAS) · 2022
In this paper, an autonomous real-time mine detection algorithm based on improved YOLOv5 in a complex underwater environment is proposed. The target recognition system based on the optical image is used to guide autonomous underwater vehicles (AUV) to achieve high precision mine exploration and assist the autonomous mine hunting weapon system to finish the mine-hunting task. In view of all kinds of various interference information of the underwater optical images, a set of image preprocessing systems has been added to the algorithm based on YOLOv5 which has the advantages of both detection accuracy and detection speed. The original data are processed by data enhancement, image enhancement, sample equalization, negative sample addition, and Poisson fusion, which can reduce the problem of poor detection results caused by the input image quality to some extent. At the same time, the number of layers of YOLOv5 is reduced, which makes the detection algorithm meet the requirements of the AUV embedded system without affecting the detection accuracy, and improves the real-time detection of the mobile terminals. The experimental results show that the proposed method could effectively improve the detection accuracy and real-time performance of the underwater mine detection system.