MFE-Net: A Novel Multiscale Feature Enhancement Network for SAR Ship Instance Segmentation

Deep learning has achieved significant progress in ship instance segmentation for synthetic aperture radar (SAR) im-ages. However, due to the challenges posed by inshore scenes, such as dense clustering, arbitrary arrangement orientation, and multi-scale targets, the performance of existing methods still falls short of expectations. To overcome the dilemma, we propose MFE-Net, a one-stage instance segmentation network based on oriented bounding box (OBB). First, we design an Orientation-aware Seg-mentation Head (OSeg Head) to enhance the network’s perception of directional features while mitigating interference from complex background. Additionally, a Feature Aggregation Diffusion Pyra-mid (FADP) is proposed, consisting of the Multi-level Feature Alignment Module (MAM) and the Multi-scale Feature Enhance-ment Module (MFEM). The FADP aggregates global semantic and detailed information, and effectively enhances the feature repre-sentations of both deep and shallow layers through multi-scale fea-ture extraction. Experimental results on two public benchmark datasets (HRSID, SSDD) and a self-built dataset (HRSID_IS) demonstrate that MFE-Net achieves an ideal trade-off between ac-curacy and efficiency, respectively reaching 58.9%, 63.0%, and 46.9% AP with parameters of 12.36M.