A deep learning framework based on SSM for detecting small and occluded objects in complex underwater environments

Abstract Regular monitoring of marine life is essential for preserving the stability of marine ecosystems. However, underwater target detection presents several challenges, particularly in balancing accuracy with model efficiency and real-time performance. To address these issues, we propose an innovative approach that combines the Structured Space Model (SSM) with feature enhancement, specifically designed for small target detection in underwater environments. We developed a high-accuracy, lightweight detection model—UWNet. The results demonstrate that UWNet excels in detection accuracy, particularly in identifying difficult-to-detect organisms like starfish and scallops. Compared to other models, UWNet reduces the number of model parameters by 5% to 390%, significantly improving computational efficiency while maintaining top detection accuracy. Its lightweight design enhances the model's applicability for deployment on underwater robots, enabling effective real-time detection of subaquatic targets.