LR-SARNET: A Lightweight and Robust Network for Multi-scale and Multi-scene SAR Ship Detection

SAR Ship image has the characteristics of complex background, blurred target edge and scale difference, which makes the target detection difficult. This article builds a lightweight and robust network for multi-scale and multi-scene SAR ship detection (LR-SARNET). Firstly, with the goal to minimize the computational complexity of the model, a lightweight backbone feature extraction network (CGNet) is designed to generate sufficiently redundant feature maps with low computational cost. Secondly, a linear feature fusion module (ENECK) is designed to efficiently fuse deep local feature maps. Finally, the extremely efficient spatial pyramid (EESP) is integrated into the target detection head, which expands the receptive field of the network. The experiment on SSDD and HRSID dataset proves that our algorithm has strong robustness and excellent generalization performance.