Abstract: Aiming at the difficulty of ship target detection in synthetic aperture radar images, a multi-scale target detection framework based on deep feature enhancement network was proposed. Darknet53 was used to extract features from original images, and build a four-scale feature pyramid from top to bottom. A feature fusion structure based on attention mechanism was specially designed to connect adjacent feature layers from bottom to top, and rebuild enhanced feature pyramid. Then, the proposed method utilized the candidate region and its surrounding context information to provide a higher quality judgment basis for the detector to calculate the classification confidence and target score.The average detection precision of the proposed method on SSDD public data set and SAR-Ship self-built data set were 94.43% and 91.92% respectively. The experimental results show that the proposed network framework is reasonable and has superior detection performance.