Class-incremental few-shot underwater object detection framework.

Underwater object detection is vital for marine exploration and ecological monitoring. However, due to the low-quality original characteristics of underwater images, the diversity changes of underwater species, and the scarcity of samples, existing underwater object detection technologies struggle to address these challenges simultaneously. To this end, this paper proposes a class-incremental few-shot underwater object detection framework(CIFS-UD). First, we design an end-to-end multi-task architecture by integrating the existing underwater image enhancement module based on the deep inception and channel-wise attention (DICAM) and object detection, enhancing underwater visual quality and enabling more discriminative feature representations. Second, we develop a prototype-based multi-scale dual attention module (ProMSDA) that strengthens cross-scale feature correlations and key object regions to optimize class prototype representations. Finally, we propose a dynamic boundary-aware prototype collaboration optimization strategy (DB-PCO) that jointly constrains intra-class compactness and inter-class separability to alleviate catastrophic forgetting. Experiments on Brackish, Trashcan, and RUOD datasets verify that CIFS-UD can effectively adapt to novel classes and improve the class-incremental few-shot underwater object detection performance.The code and implementation are publicly available at https://github.com/snow102/Class-Incremental-Few-Shot-Underwater-Object-Detection-Framework .