Abstract: To address the challenges of background complexity and target scale changes in synthetic aperture radar (SAR) images, especially in densely populated small-target scenes prone to false and missed detections, a multi-granularity feature and shape-position similarity metric method for ship detection in SAR images is proposed. First, a multi-granularity feature aggregation structure containing two branches is designed in the feature extraction stage. One branch decomposes the feature map cascade by Haar wavelet transform to expand the global receptive field to extract coarse-grained features. The other branch introduces spatial and channel reconstruction convolution to capture detailed texture information, thereby minimizing the loss of contextual information. The two branches effectively suppress the complex background and clutter interference by synergistically exploiting the interaction of local and non-local features to achieve accurate extraction of multi-scale features. Next, by utilizing the Euclidean distance and combining position and shape information, we propose a shape-position similarity metric to solve the problem of position deviation sensitivity in small target-dense scenes, thereby balancing the allocation of positive and negative samples. In a comprehensive comparison with 11 detectors from one-stage, two-stage, and DETR series on the SSDD and HRSID datasets, our method achieves mAP scores of 68.8% and 98.3%, and mAP50 scores of 70.8% and 93.8%, respectively. In addition, our model is highly efficient, with just 2.4 M parameters and a computational load of only 6.4 GFLOPs, outperforming the comparison methods. The proposed method shows excellent detection performance under complex backgrounds and ship targets of different scales. While reducing the false detection rate and missed detection rate, it has a low model parameter amount and computational complexity.