At present， the target detection technology based on deep learning algorithm has achieved the remarkable results in ship detection of Synthetic Aperture Radar （SAR） images. However， there is still the problem of poor detection effect of small target ships and densely arranged ships near shore. To solve the above problem， a new ship detection algorithm based on improved RetinaNet was proposed. On the basis of traditional RetinaNet algorithm， firstly， the convolution in the residual block of feature extraction network was improved to grouped convolution， thereby increasing the network width and improving the feature extraction ability of the network. Then， the attention mechanism was added in the last two stages of feature extraction network to make the network more focus on the target area and improve the target detection ability. Finally， the Soft Non-Maximum Suppression （Soft-NMS） was added to the algorithm to reduce the missed detection rate of the algorithm for the detection of densely arranged ships near shore. Experimental results on High-Resolution SAR Images Dataset （HRSID） and SAR Ship Detection Dataset （SSDD） show that， the proposed algorithm effectively improves the detection effect of small target ships and near-shore ships， is superior in detection precision and speed compared with the current excellent object detection models such as Faster Region-based Convolutional Neural Network （R-CNN）， You Only Look Once version 3 （YOLOv3） and CenterNet.