Enhanced Underwater Object Detection via Multi-Scale Attention and Adaptive Feature Fusion

Underwater target detection faces challenges due to poor image quality, multi-scale variations, and occlusion. This study introduces a novel model integrating multi-scale attention and adaptive feature fusion to enhance detection accuracy. Leveraging the DEIM framework, we propose a Multi-Scale Attention Block (MSAB) for improved feature extraction across scales, a Lightweight Sparse Self-Attention Block (LSSA) for noise suppression, an Adaptive Weighted Downsampling Block (AWDS) for information preservation, and a Context-Guided Feature Fusion Module (CGFM) for intelligent feature integration. Experimental results on URPC and DUO datasets demonstrate significant improvements in AP, AP50, and AP75 metrics compared to the baseline model DEIM, with gains of 3.2%, 3.3%, and 3.4% on URPC, and 3.9%, 3.6%, and 4.7% on DUO, respectively. These findings underscore the effectiveness of our approach in addressing the complexities of underwater environments. https://github.com/EUOD/MSAuod.