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.

翻译：作为最重要的水下感知技术之一，前视声纳具有独特的成像特性。声纳图像常受到严重散斑噪声、低纹理对比度、声学阴影和几何畸变的影响。这些因素使得传统教师-学生框架在极有限标注数据条件下难以在声纳语义分割任务中取得满意性能。为解决该问题，我们提出面向前视声纳图像的协同教师语义分割框架。该框架引入由一名通用教师与多名声纳专用教师构成的多教师协同机制。通过采用多教师交替引导策略，学生模型既能学习通用语义表征，又能同时捕获声纳图像的独特特征，从而实现更全面鲁棒的特征建模。针对声纳图像挑战易导致教师生成大量含噪伪标签的问题，我们进一步设计了跨教师可靠性评估机制。该机制通过评估多视角多教师预测的一致性及稳定性动态量化伪标签的可靠性，从而缓解噪声伪标签带来的负面影响。值得注意的是，在FLSMD数据集上，当仅使用2%标注数据时，本方法相比其他最先进方法在mIoU上提升了5.08%。