Abstract: The abundant contextual information in synthetic aperture radar (SAR) images remains underutilized in deep learning-based ship detection. Hence, this study proposed a novel method for detecting ships from SAR images based on high-dimensional contextual attention and dual receptive field enhancement. The dual receptive field enhancement was employed to extract multi-dimensional feature information from SAR images, thereby guiding the dynamic attention matrix to learn rich contextual information during the coarse-to-fine extraction of high-dimensional features. Based on YOLOv7, a YOLO-HD network was constructed by incorporating a lightweight convolutional module, a lightweight asymmetric multi-level compression detection head, and a new loss function,XIoU. A comparative experiment was conducted on the E-HRSID and SSDD datasets. The proposed method achieved average detection accuracy of 91.36 % and 97.64 %, respectively, representing improvements by 4.56 and 9.83 percentage points compared to the original model, and outperforming other classical models.