To solve the problems of low detection rate and high false alarm rate caused by clutter interference in the detection of inshore ship targets in Synthetic Aperture Radar (SAR) images, an SAR image ship detection algorithm for complex inshore scenarios is proposed. the target feature extraction ability of the feature extraction network is enhanced by designing C2f-EMBC and BasicStage based on YOLOv8n, which captures the geometric feature information of the ships, and makes the network pay more attention to detailed features. Meanwhile, a Global Receptive Field Spatial Pyramid Pooling Fast (GRF-SPPF) algorithm is proposed, which combines important information of the feature layer with background information of global receptive field. In the feature fusion structure, AKConv and Slim-Neck are used to design the AVSFPN neck structure for feature fusion at different levels. In addition, the WIoU loss function is used to improve the convergence rate and generalization of the model. The testing experiments are conducted on the BBox-SSDD and HRSID datasets. The results show that: the improved algorithm has an mAP0. 5 of 96. 4% and 77. 4% respectively on the BBox-SSDD and HRSID inshore test set, which is increased by 4. 8 and 4 percentage points respectively in comparison with the original YOLOv8n. The results prove the effectiveness of the proposed method in improving the accuracy of ship target detection in SAR images in inshore scenarios.