Abstract: Synthetic Aperture Radar (SAR) has a wide range of applications in military and civilian sectors with all-weather, all-day observation capabilities.Considering the cost and efficiency of SAR research and the application of SAR image in the field of target detection, SAR image simulation technology plays an important role.In view of the time-consuming problem of traditional imaging methods, this paper uses the rapid imaging technology based on SBR to obtain SAR images in large quantities quickly. The data source generated by this method is more complete than the measured data set.In order to identify targets in SAR images more accurately, the original algorithm framework was improved by changing the initial size of the candidate box according to real ship targets and using feature fusion based on the target detection network of Faster RCNN.Finally, Feature pyramid networks (FPN) is added to Faster RCNN framework to further improve the ability of target recognition algorithm to detect and recognize ship targets in SAR image.