In response to the problems of low detection accuracy, missed detections, large model size unsuitable for deployment, and low real-time performance of existing algorithms in synthetic aperture radar (SAR) image ship target detection tasks, an improved ship target detection model based on YOLOv8s is proposed for lightweight SAR images. First, a lightweight residual feature enhancement module ACC, which uses adaptive pooling, is proposed to extract different contextual information. The residual enhancement method reduces the loss of feature information at the highest level in the feature pyramid, improves the expression of high-level features, and enhances the network's ability to detect small targets. Subsequently, lightweight dynamic snake-shaped convolution (DSConv) is introduced to replace the standard convolution operation, thereby improving detection performance of the model for small-strip ship targets and reducing missed target detection. Finally, the lightweight BiFormer dynamic sparse attention module is integrated to further optimize the small target detection effect. On the SAR ship detection dataset (SSDD), the algorithm proposed in this paper outperforms the original algorithm in precision, recall, and mAP@0.5, by 5.0, 3.6, and 3.3 percentage points, respectively, and model detection ability for ship targets in SAR images is significantly enhanced. The generalized experimental results of the model on the high-resolution SAR images dataset (HRSID) show better performance than those of other classical algorithms.