Aiming at the problem that the nearshore ship targets in synthetic aperture radar (SAR) images were affected by the background clutter, that resulted in lower detection rate of ship targets in SAR images and higher false alarm rate and missed detection rate of smallscale ship targets, an improved feature pyramid network (FPN) model for nearshore ship target detection in SAR images under complex backgrounds was proposed based on the FPN target detection algorithm. The deformable convolution was used in the feature extraction network to determine the target sampling point position more accurately to enhance the target feature extraction ability and improve the detection rate of ship targets in SAR images under complex backgrounds. At the same time, the channel attention mechanism was used to capture the feature dependencies relationship between different channel graphs in the feature extraction network and reduce the missed detection rate. Test experiments on the public SAR image ship dataset show that the detection accuracy of the model in complex scenes is 87.95%, which is 8.46% higher than the original FPN. There in, the detection accuracy for smallscale ship targets is 95.14%,which is 5.28% higher than the original FPN. Compared with Yolo5 and mask RCNN, the average detection accuracy of the improved FPN model increases by 11.21% and 2.98% respectively.