Deep learning methods such as convolutional neural networks (CNN) have been widely used in fields of image processing and object recognition.However,the SAR images cannot yet be efficiently detected by CNN methods.Compared with traditional images,the SAR images have the advantage of all-day and all-weather acquisition,but they cannot obtain enough annotation due to the difficulty for interpretation and short of users.This paper proposes a SAR target detection method based on scene synthesis and anchor constraint.Firstly,the target and its shadow are segmented by region growing as well as threshold algorithm,and then the target detection data set is synthesized by randomly embedding the reasonable region into the SAR complex scene.Considering the SAR target's geometric characteristics and image resolution parameters,the anchor's size of Faster-RCNN is constrained to reduce the candidate frames that cannot meet the SAR target detection frame size,which massively reduce redundancy calculations so as to improve the efficiency and accuracy of training and testing process.