Ship detection through synthetic aperture radar (SAR) imagery is essential for maritime monitoring, security, and navigation. However, SAR images present several challenges, including noise, low contrast, and varying ship sizes. In this paper, we propose an architecture that incorporates ResNet-50 as the backbone for multi-scale feature extraction, which is further enhanced using a convolutional block attention module for refining spatial and channel-wise features. Feature combination and attention fusion mechanisms are employed to integrate critical features while suppressing irrelevant information, and detection heads are optimized for precise bounding box regression and classification. This design ensures improved efficiency and accuracy, demonstrating significant advancements in SAR ship detection performance. Extensive experiments conducted on the SAR ship detection dataset and the high-resolution SAR image dataset yielded AP50 scores of 98.1% and 91.8%, respectively, outperforming several state-of-the-art detectors, thus validating the effectiveness of the proposed method.
Thi et al. (Tue,) studied this question.