Deep learning has advanced ship instance segmentation in synthetic aperture radar (SAR) imagery. However, segmentation performance remains limited by multi-scale ship variations, inaccurate small-target segmentation, and significant background interference in complex scenes. To address these challenges, we propose a novel ​​dynamic attention and context fusion network (DACF-Net)​​, which enhances mask prediction through geometry-aware multi-scale progressive feature refinement. DACF-Net integrates multi-scale context aggregation, cross-level semantic guidance, and geometry-aware proposal generation to improve ship representation and segmentation accuracy. Specifically, a multi-scale feature fusion module (MSFFM) is introduced to aggregate scale-adaptive contextual information through a multi-branch large-kernel convolution structure. A cross-level feature interaction module (CLFIM) modulates low-level features with high-level semantics to optimize cross-level semantic consistency. A dynamic context perception block (DCPBlock) jointly models mid-range spatial dependencies and global channel-wise context to suppress background interference. In addition, three variants are developed to balance segmentation accuracy and computational efficiency. Experiments on HRSID and PSeg-SSDD demonstrate the effectiveness of the proposed approach. DACF-Net‡​ achieves segmentation AP​ values of ​​63.3%​​ and ​​65.7% on HRSID and PSeg-SSDD, respectively, outperforming the second-best method by 4.2% and 1.4%. Further experimental results show that DACF-Net and its variants achieve competitive segmentation performance for ship targets across different scales.

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