Aiming at the problems of poor detection accuracy and large amount of computation in the existing SAR ship target detection methods,a lightweight ship target detection method based on YOLOv5 and GhostNet is proposed.The GhostConv and GhostC3 modules of the lightweight network GhostNet are introduced to improve the backbone network of YOLOv5,achieving a significant reduction in model computation.The CBAMC3 module is introduced in the neck network to adjust attention during the feature fusion stage and achieve accurate target detection.In addition,the EIoU loss function is introduced to improve the regression accuracy and rate of convergence of the prediction box.The test results on the public dataset indicate that the improved algorithm significantly reduces the number of parameters and model volume while maintaining high accuracy,making it an ideal lightweight ship detection model for SAR images.