Recursive Signal Amplification and Multi‐Dimensional Feature Rectification for Maritime Small Object Perception

ABSTRACT

Detecting small objects in maritime scenes is critical for Maritime Autonomous Surface Ships (MASS) but remains difficult because vast low‐entropy sea–sky backgrounds dilute the weak feature response of tiny targets in global vision transformers. To address this, the Recursive Signal Amplification Network (RSGN) is proposed. A Hierarchical Context‐Scale Alignment (HCSA) stream couples a Swin Transformer backbone with multi‐dimensional attention to align semantic features across scale, space, and channel. A Saliency‐Guided Recursive Gating (SSRG) module then estimates a spatial saliency prior and recursively amplifies the signal‐to‐noise ratio of potential small targets before final detection. On the public WSODD benchmark, RSGN achieves 89.5% mAP and 36.3% APS, surpassing the baseline DINO by 6.9% and 5.9%, respectively.