Class-Incremental Learning-Based Few-Shot Underwater-Acoustic Target Recognition

This paper proposes an underwater-acoustic class-incremental few-shot learning (UACIL) method for streaming data processing in practical underwater-acoustic target recognition scenarios. The core objective is to expand classification capabilities for new classes while mitigating catastrophic forgetting of existing knowledge. UACIL’s contributions encompass three key components: First, to enhance feature discriminability and generalization, an enhanced frequency-domain attention module is introduced to capture both spatial and temporal variation features. Second, it introduces a prototype classification mechanism with two operating modes corresponding to the base-training phase and the incremental training phase. In the base phase, sufficient pre-training is performed on the feature extraction network and the classification heads of inherent categories. In the incremental phase, for streaming data processing, only the classification heads of new categories are expanded and updated, while the parameters of the feature extractor remain stable through prototype classification. Third, a joint optimization strategy using multiple loss functions is designed to refine feature distribution. This method enables rapid deployment without complex cross-domain retraining when handling new data classes, effectively addressing overfitting and catastrophic forgetting in hydroacoustic signal classification. Experimental results with public datasets validate its superior incremental learning performance. The proposed method achieves 92.89% base recognition accuracy and maintains 68.44% overall accuracy after six increments. Compared with baseline methods, it improves base accuracy by 11.14% and reduces the incremental performance-dropping rate by 50.09%. These results demonstrate that UACIL enhances recognition accuracy while alleviating catastrophic forgetting, confirming its feasibility for practical applications.