This paper proposes a ship detection network called the Long and Short path Feature Fusion Network (LSFF-Net) to address the challenges of detecting small and inshore samples in SAR image ship detection tasks. In LSFF-Net, the Long and Short path Feature Fusion Block (LSFF-Block) makes the model compatible with different scale target information. The application of structural re-parameterization enriches the module learning ability, and the multi-scale features are fused with the feature pyramid network. To address inshore target detection, a data redistribution algorithm is designed to increase detection accuracy of nearshore targets. The experimental results show that the proposed algorithm fully learns the information of the image and is more in line with the characteristics of SAR images. The average precision (AP) of the algorithm reaches 97.50 % in the public data set detection results, which is better than the mainstream target detection algorithm. LSFF-Net provides a new solution for improving the accuracy of small and inshore target detection in SAR images.