NST-YOLO11: ViT Merged Model with Neuron Attention for Arbitrary-Oriented Ship Detection in SAR Images

Due to the significant discrepancies in the distribution of ships in nearshore and offshore areas, the wide range of their size, and the randomness of target orientation in the sea, traditional detection models in the field of computer vision struggle to achieve performance in SAR image ship target detection comparable to that in optical image detection. This paper proposes an oriented ship target detection model based on the YOLO11 algorithm, Neural Swin Transformer-YOLO11 (NST-YOLO11). The proposed model integrates an improved Swin Transformer module called Neural Swin-T and a Cross-Stage connected Spatial Pyramid Pooling-Fast (CS-SPPF) module. By introducing a spatial/channel unified attention mechanism with neuron suppression in the spatial domain, the information redundancy generated by the local window self-attention module in the Swin Transformer Block is cut off. Furthermore, the idea of cross-stage partial (CSP) connections is applied to the fast spatial pyramid pooling (SPPF) module, effectively enhancing the ability to retain information in multi-scale feature extraction. Experiments conducted on the Rotated Ship Detection Dataset in SAR Images (RSDD-SAR) and the SAR Ship Detection Dataset (SSDD+) and comparisons with other oriented detection models demonstrate that the proposed NST-YOLO11 achieves state-of-the-art detection performance, demonstrate outstanding generalization ability and robustness of the proposed model.