Underwater Object Detection based on YOLO-v3 network
Yanmei Wang, Jiaxin Liu, Siquan Yu, Kai Wang, Zhi Han, Yandong Tang
Abstract
Recently, side scan sonar (SSS) is increasingly applied to underwater search, which can display the microgeomorphic morphology and distribution, and obtain a continuous two-dimensional submarine acoustic map with a certain width. Automatic underwater object detection methods can help a lot in case of long searches, where sonar operators may feel exhausted and therefore miss the possible object. This paper proposes an underwater object detection method based on YOLO-v3 network. We first establish a real side scan sonar image data-set, which includes 7000 sonar images with four types of objects. Secondly, we propose an underwater object detection system based on side scan sonar images and YOLO-v3 network. Finally, we carried out extensive experiments in the real underwater environment to prove the effectiveness of our algorithm. Our work indicates that the YOLO-v3 network is an effective way to improve the accuracy of underwater object detection.