Target Detection in Side Scan Sonar Images Based on YOLOv3 Anchor Boxes Optimization

‍ ‍The use of side scan sonar images to detect submarine targets was of great significance to the exploitation of marine resources and marine military protection. At present， the traditional machine learning method of artificially extracting image features for target detection was gradually replaced by deep learning. Deep learning technology improved the efficiency of image target detection while reducing the complexity of the algorithm， which greatly promoted the development of target detection technology. When deep learning detection algorithms were applied to the field of side scan sonar image target detection， anchor boxes were important as prior information in the target detection network which affected the final detection performance. Considering the problem that real target boxes of the sonar dataset and anchor boxes set by the network may not fit， this paper optimized anchor boxes on the basis of YOLOv3， and proposed a strategy that can obtain effective prior anchor boxes. Firstly， real target boxes were clustered using the K-Means algorithm to obtain anchor boxes that fit the sonar data set， and then a hyperparameter mapping relationship was designed to stretch and transform the clustered anchor boxes. In such case， the obtained anchor boxes not only contained the target information of the sonar dataset but also took advantage of the multi-scale features of YOLOv3. The experimental results show that the proposed anchor boxes optimization strategy can make the YOLOv3 network obtain better detection performance， which is suitable for the problem of side scan sonar image target detection.