Abstract: [Purposes] To address the challenges of low accuracy in traditional bottom detection methods using side scan sonar data,which leads to difficulties such as image stitching problems and shape distortion,this study aims to achieve high-accuracy and stable real-time bottom detection for side scan sonars. [Methods] Accordingly,a dual-channel one-dimensional real-time bottom detection model based on the U-Net architecture (D1D-UNet)is proposed.The model takes dual-channel sequential data from the port and starboard sides of the side-scan sonar as input,enhancing its ability to capture both global and local features of the sonar data,thereby significantly improving bottom detection accuracy. [Findings] Experiments conducted on measured datasets from Meizhou Bay and Bayuquan demonstrate that the model achieved maximum accuracies of 97.26% and 96.16%,respectively.Compared with existing mainstream methods,the detection accuracy on the three test sets of Meizhou Bay improved by 5.35,6.58,and 12.05 percentage point. [Conclusions] This study provides an effective solution for real-time bottom detection using side scan sonars and further expands the application scope of side-scan sonars．

Key words: side scan sonar, bottom detection, deep learning