For marine vessel target detection in SAR images，deep learning technology alone usually has difficulty in satisfying the detection requirements in accuracy and timeliness. Vessel targets in SAR image are of small sizes and resolutions，which are easily interfered by noise and spot interruption. It is challenging to extract subtle features and eliminate background interference under complex conditions. To overcome the above problems，we propose an end-to-end new detection model based on YOLOv3 framework. The residual module structure is used to avoid network degradation. Combined with deep and shallow feature detection of different target sizes，we extract network parameters for basic features to avoid training from scratch. At the same time，according to the characteristics of small vessel targets in SAR image，the neural network structure is further optimized to achieve fast target detection and categorization in wide-area SAR images，and the detection model is compressed and light-weighted. We construct and utilize a SAR image dataset with different vessel targets for target detection and classification test. The experimental results show that the proposed detection method shows significant anti-jamming ability and detection performance in complex scenes.