In order to solve the problems of low target detection accuracy and difficulty in small target detection caused by blurred images and lack of texture features in the existing mainstream algorithms for ship target detection in SAR images, and considering that the real-time performance of the network will be affected by introducing too many parameters, an improved YOLOv7 ship target detection method based on coordinate attention mechanism and Normalized Wasserstein Distance (NWD) metric is proposed. Firstly, the maximum pooling and residual structure are introduced into the coordinate attention mechanism to improve the model feature extraction ability. Secondly, combining dense connection with lightweight convolution, SPPCSPC-P is designed to enhance the fusion between features. In addition, a small target detection layer is added to the backbone network to improve the low detection accuracy of the model for small targets. Finally, the weighted positioning loss function is designed by using NWD metric and CIoU loss, which further improves the model detection accuracy. Experiments are carried out on SSDD dataset, and the experimental results show that the average accuracy of this method reaches 98.38%, which is 2.09 percentage points higher than that of YOLOv7 network.