A Lightweight OBB-Based SAR Ship Detection Network Based on Feature Enhancement and Cross-Scale Fusion

Ship detection in synthetic aperture radar (SAR) imagery plays an important role in maritime surveillance and security applications. However, in complex near-shore and port scenes, strong background clutter, significant variations in target scale, and the need for efficient feature representation make it challenging to achieve a good balance between detection accuracy and computational efficiency. To address these challenges, this paper proposes a lightweight oriented bounding box (OBB)-based SAR ship detection network based on feature enhancement and cross-scale fusion. By systematically redesigning key components, including feature enhancement, downsampling modeling, multi-scale fusion, and oriented target representation, the proposed method achieves coordinated optimization of accuracy and efficiency in complex scenarios. Specifically, a Multi-Scale Coordinate-Channel Attention mechanism (MSCCA) is constructed to enhance discriminative ship features while suppressing background interference. An Adaptive Dual-pooling Fusion Downsampling strategy (ADFD) is then introduced to reduce spatial resolution while alleviating fine-grained feature loss. In addition, a Lightweight Adaptive Multi-Scale Feature Enhancement module (LAMFE) is designed to improve multi-scale modeling capability under strict computational constraints. Finally, a Lightweight Oriented Bounding Box detection head (LwOBB) is employed to enable accurate localization of ship targets with arbitrary orientations. The experimental results on the publicly available R-SSDD and RSDD-SAR datasets show that the proposed detection network achieves higher detection accuracy while maintaining a lightweight structure. The model contains only 2.06 M parameters and has a computational complexity of 5.7 GFLOPs. Compared with several mainstream detection methods, the proposed method has achieved significant improvements in detection accuracy and effectively reduces the occurrence of missed detections and false alarms.