An Unsupervised Obstacle Segmentation Method for Forward-Looking Sonar Based on Teacher–Student Transfer Learning

Facing the challenges of scarce annotations in forward-looking sonar image segmentation, this paper proposes a teacher–student network for unsupervised domain adaptation. The proposed model undergoes supervised learning with optical image data to endow the student model with basic segmentation capabilities, using the segment anything model (SAM) as the teacher to generate pseudo-labels in the sonar image domain, thus achieving knowledge transfer without relying on annotated sonar images. An adaptive weighting approach is proposed, which generates a consistency map using predictive consistency in the source domain and the target domain to assess the quality of pseudo-labels. This method dynamically adjusts supervisory strength, preventing incorrect fitting caused by noisy pseudo-labels. In addition, a multi-scale attention module is designed to refine bottleneck features of the U-Net. The effectiveness of the proposed method is validated on a self-built public forward-looking sonar image dataset, achieving a mean intersection over union (IoU) of 40.8% and a mean average precision (mAP) of 70.3%, demonstrating significant improvements over existing typical UDA methods.