Detection of submarine pipeline and cable targets based on depth feature of high resolution sonar image

For Side-Scan Sonar (SSS) submarine pipeline and cable target feature extraction, there are some problems such as poor real-time performance, high false detection rate, and difficulty in deploying edge equipment.With deep feature technology,this study applies a deep neural network to detect submarine pipeline and cable targets in order to solve the above problems.To enable real-time detection of submarine pipelines and cable in SSS imagery, we improve the YOLO11n-seg model by incorporating the A2C2f and DSConv modules, leveraging the characteristic features of the target images. It reduces the false detection rate of submarine pipeline feature in SSS image,the size of parameters and realize lightweight deployment.In allusion to the Marine-PULSE submarine pipeline and cable dataset,ablation experiments and comparative experiments are designed. The experimental results show significant improvements over the original YOLO11n-seg model. Specifically,the modified model bounding box recall improved by 9.7%,and mAP@50-95 improved by 1.6%; instance segmentation recall improved by 10.3%,and mAP@50 improved by 3.6%. The detection precision and integrity are enhanced synchronously, and the size of parameters is reduced by 15%, which has stronger advantages in real-time performance. Regarding object detection, our model demonstrates superior performance, with its mAP@50 improved by 5.2% compared to YOLO12n-seg and by 12.5% compared to YOLO13n-seg. Experiments show that the model designed in this study is an effective method for real-time detection of SSS submarine pipeline and cable targets, and has a good development prospect and promotion.