FDBANet: A Fusion Frequency-Domain Denoising and Multiscale Boundary Attention Network for Sonar Image Semantic Segmentation

Accurate semantic segmentation of sonar terrain images can identify and classify various structures on the seabed, which plays a vital role in marine exploration and construction. However, challenges such as high noise interference, low image resolution, and blurry target boundaries in sonar images limit the performance of semantic segmentation models that are typically designed for optical images. To address these issues, we propose an encoder-decoder network named FDBANet that integrates frequency-domain denoising and multiscale boundary feature extraction modules. We solve the problem of large noise in sonar images from the perspective of the frequency domain. In addition, we combine traditional boundary detection methods with deep convolutional networks to construct a multiscale boundary feature extraction module, which enhances the ability to reconstruct object boundaries in sonar images. To verify the effectiveness of the proposed model, we construct a sonar terrain image segmentation dataset and conduct comparative experiments. The results show that FDBANet achieves effective multiobject segmentation in sonar images while maintaining low computational complexity. In addition, ablation experiments were conducted on the proposed network to further verify the importance and effectiveness of each module.