Abstract: In ship detection tasks,SAR images are widely used in maritime resource management,search and rescue,and other scenarios due to their excellent imaging conditions.However,traditional target detection algorithms perform poorly due to issues such as the small size of ships and sea surface clutter.Recently,many algorithms have introduced the attention mechanism of Transformer to achieve better semantic interpretation or adopted more complex network structures to improve feature extraction capabilities.This has improved detection accuracy to some extent but has sacrificed detection speed.This paper proposes a ship detection method for SAR images based on small target feature enhanced RT-DETR.The method consists of three parts:1)Large model prompt generation network:Leveraging the zero-shot learning capability of multimodal large models,prompts are generated to extract more discriminative information from the image modality;2)AIFI-EAA module:Using RT-DETR as the baseline,the scale-invariant feature interaction module is improved by introducing an efficient additive attention mechanism to reduce the computational complexity of the algorithm;3)Lightweight small target feature enhancement fusion network:A small target detection layer is added to the multi-scale feature fusion network,and the CSP-OmniKernel module is designed for multi-scale feature fusion to enhance small target detection performance.Experiments on three public datasets(SSDD,HRSID,and SAR-Ship-Dataset) demonstrate that the proposed method has advantages in terms of accuracy.

Key words: Ship detection,SAR image,Lightweight,RT-DETR,Small target detection