CTFS : Collaborative Teacher Framework for Forward-Looking Sonar Image Semantic Segmentation with Extremely Limited Labels

Abstract

As one of the most important underwater sensing technologies, forward-looking sonar exhibits unique imaging characteristics. Sonar images are often affected by severe speckle noise, low texture contrast, acoustic shadows, and geometric distortions. These factors make it difficult for traditional teacher-student frameworks to achieve satisfactory performance in sonar semantic segmentation tasks under extremely limited labeled data conditions. To address this issue, we propose a Collaborative Teacher Semantic Segmentation Framework for forward-looking sonar images. This framework introduces a multi-teacher collaborative mechanism composed of one general teacher and multiple sonar-specific teachers. By adopting a multi-teacher alternating guidance strategy, the student model can learn general semantic representations while simultaneously capturing the unique characteristics of sonar images, thereby achieving more comprehensive and robust feature modeling. Considering the challenges of sonar images, which can lead teachers to generate a large number of noisy pseudo-labels, we further design a cross-teacher reliability assessment mechanism. This mechanism dynamically quantifies the reliability of pseudo-labels by evaluating the consistency and stability of predictions across multiple views and multiple teachers, thereby mitigating the negative impact caused by noisy pseudo-labels. Notably, on the FLSMD dataset, when only 2% of the data is labeled, our method achieves a 5.08% improvement in mIoU compared to other state-of-the-art approaches.

Figures (8)

• Figure 1: The unique characteristics of sonar images lead to limited adaptability in conventional teacher–student frameworks, making them ineffective for handling semantic segmentation tasks under extremely scarce annotation conditions.
• Figure 2: (a) The overall architecture of CTFS, where knowledge is transferred to the student through the collaboration between the traditional teacher and the sonar teacher. (b) The multi-view reliability assessment process of pseudo-labels.
• Figure 3: (a) Example of shadows in sonar images: due to the obstruction of objects during sonar propagation, a shadow is formed behind the object. The green hollow area represents the GT, and the red area represents the shadow behind the target. (b) Example of how sonar energy decreases with propagation distance. In the right-hand image, the solid blue line represents the vertical energy distribution in the left-hand image, and the dashed red line is a linear regression fit to the blue line.
• Figure 4: (a) The collection process of the FSSG dataset. (b) Sample distribution of each category in the FSSG dataset, and the visualization of targets under sonar and visible perspectives.
• Figure 5: Qualitative demonstrations of different approaches on the FLSMD dataset with 2% labeled data.