Physics-Guided Sonar Image Fine-grained Recognition under Scarce Annotations

Sonar image recognition is a key technology in underwater exploration systems. Compared with natural images, sonar images have fewer texture details and are easily affected by heavy noise, making it more challenging for specialists to distinguish the subtle differences among classes. In view of this, studying fine-grained classification methods for sonar images with scarce annotations is of significant importance. To address this issue, we propose a Physics-Guided Teacher-Student (PGTS) framework to explore the unique physical information of sonar images while simultaneously mitigating the effects of limited annotations. First, PGTS reconstructs sonar signals through physical simulation and a specially designed physics-guided feature generation module, which allows it to bypass the time-consuming physical simulation during inference. Then, we design a multi-modal teacher model combines the reconstructed sonar signals and sonar images to extract discriminative features to generate robust pseudo labels for fine-grained target categories. Finally, the knowledge is transferred to a single-modal student model through consistency loss. Under the joint constraints of the teacher model and the reconstructed sonar physical signals, the student model continuously improves its performance in annotation-scarce scenarios. Notably, when merely 1% of the data is labeled, our method outperforms other state-of-the-art approaches by 12.46% in terms of accuracy.