Side-scan sonar underwater target segmentation using the BHP-UNet

Although target detection algorithms based on deep learning have achieved good results in the detection of side-scan sonar underwater targets, their false and missed detection rates are high for multiple densely arranged and overlapping underwater targets. To address this problem, a side-scan sonar underwater target segmentation model based on the blended hybrid dilated convolution and pyramid split attention UNet (BHP-UNet) algorithm is proposed in this paper. First, the blended hybrid dilated convolution module is adopted to improve the ability of the model to learn deep semantics and shallow features while improving the receptive field. Second, the pyramid split attention module is introduced to establish a long-term dependency between global and local information while processing multi-scale spatial features. Three sets of experimental results show that the BHP-UNet model proposed in this paper has better segmentation performance than the conventional fully convolutional network, UNet, and DeepLabv3+ models, and it is able to segment dense and overlapping targets to a certain extent. The proposed model will have significance as a guide for practical applications.