To enhance the anti-jamming performance of ship detection, an effective and stable single-stage ship detection network is proposed in this study. The network is composed of three modules: feature optimization, feature pyramid fusion, and context enhancement modules. The feature optimization module extracts multi-scale context information and further refines the high-level feature input characteristics, to enhance the performance of dim–small object detection. The feature pyramid fusion module can generate semantic information with stronger representation ability. The context enhancement module integrates local and global features to enhance the network feature expression ability, reduce the impact of a complex background on detectability, adjust the imbalance between the foreground and background, and eliminate the impact of scale-wave. To verify the effectiveness and robustness of the proposed method, qualitative and quantitative verifications are performed on a self-built dataset. Experimental results show that the proposed network achieves optimal performance compared with the latest benchmark comparison model and considerably improves the detection accuracy without increasing complexity.