Synthetic Aperture Radar (SAR) ship detection is important for maritime surveillance and maritime security. However, existing methods still suffer from insufficient backbone representation, inadequate directional structure modeling, and limited cross-scale interaction under complex backgrounds. To address these issues, we propose a Direction-Aware Feature Enhancement Network (DAFE-Net). First, a Multi-Branch Feature Interaction Module (MBFIM) is designed to improve the collaborative representation of global structures and local details. Second, a Direction-Aware Contrast Enhancement Module (DACEM) is introduced to explicitly model the directional bright–dark coupled structures of SAR ships, thereby improving target–background discrimination under complex clutter. Finally, a Feature-Focused Diffusion Pyramid Network (FFDPN) is constructed to strengthen cross-scale feature interaction and improve the detection of multi-scale ship targets. Experimental results show that the proposed method outperforms several competitive detectors on the merged SSDD and HRSID dataset. Compared with DEIM-D-FINE, our method improves AP by 3.1% and APL by 5.0%. These results demonstrate that the proposed method provides an effective direction-aware modeling approach for SAR ship detection.
Zeng et al. (Wed,) studied this question.