In SAR ship dataset,small objects only account for a small proportion of the images pixels,the objects cannot be clearly recognized,and the detection efficiency is low.To solve the problems,an improved YOLOv7 based SAR ship target detection algorithm STSD-YOLO is proposed.Firstly,according to the characteristics of SAR images,the network structure is redesigned,and the relationship between multi-scale feature fusion and feature extraction is changed to solve the problem of losing detailed features due to excessive down-sampling times.Secondly,a lightweight attention mechanism named Shuffle Attention is used to fuse feature grouping with channel replacement based on the spatial domain and channel domain attention mechanism to improve the feature extraction ability of the network and reduce computational complexity.Then,the convolution variant DSConv is introduced to reduce computational intensity by storing only integers in the variable quantization kernel.Finally,the NWD metric is added to improve the performance of small object detection by modeling the bounding box as a 2D Gaussian distribution to measure the similarity between the bounding boxes of small objects.The HRSID ship dataset is adopted for experimental verification.In comparison with the baseline algorithm,the STSD-YOLO algorithm has its mAP increased by 9.9%in the ship detection task,and model volume reduced by 62.55%.Through comparative experiments,it is shown that the improved algorithm has better detection effects than other mainstream algorithms.It can effectively address the difficulties of SAR image detection,which is competent to carry out the ship detection task in SAR images.