The ship has become an important monitoring target in maritime military field.Ship target detection in SAR images suffers from poor detection effects, large computation amount and weak generalization capability.To solve the problems, a lightweight ship target detection algorithm based on YOLOv5 and Mobilenetv3 is proposed.Firstly, Mobilenetv3 backbone network is introduced to reduce the computation amount and volume of the model and realize lightweight processing of the model.Then, the EIoU loss function is introduced to improve the regression accuracy and convergence speed of the prediction box.Finally, CBAM is introduced into the neck network, and attention adjustment is conducted at the stage of feature fusion to improve the detection accuracy and detection effects of the model.The experimental results on SSDD dataset show that the volume of the improved algorithm model is reduced to 18.32% of that of the original YOLOv5 model, the training time is shortened by 35.22%, the parameter quantity is reduced to 15.94% of that of the original model, the computation amount is reduced to 10.76% of that of the original model, and mAP is improved to 98.3%.The experimental results show that the improved algorithm greatly reduces parameter quantity, computation amount, model volume and training time while maintaining high-precision detection effects, which can realize real-time detection of ship targets in SAR images.