CSS-YOLO: A SAR Image Ship Detection Method for Complex Scenes

Advances in synthetic aperture radar (SAR) technology, combined with innovations in deep learning algorithms, have established SAR as a critical method for ship detection. However, in some complex environments, such as harbors and inshore areas, false and missed detections tend to occur because of intricate background interference and the minute proportions of ship targets. To overcome these issues, this paper presents a ship detection model, CSS-YOLO, for SAR images in complex scenes. First, a vertically compressed backbone network (VCCB) is proposed, which reduces feature loss and enhances the extraction of detailed features by decreasing the number of downsampling and increasing the feature extraction width. Second, a wavelet convolution-based preprocessing downsampling module (WPCM) is proposed to obtain a larger receptive field and reduce noise to obtain finer features. Third, a short-range aggregated decentralized necking network (SADNet) is proposed to reduce the loss of detail, thereby improving the efficiency of feature fusion. To validate the effectiveness of the method, this paper conducts experiments on three datasets, HRSID, SSDD, and LS-SSDD-v1.0, for performance evaluation. Compared with other widely used methods, CSS-YOLO demonstrates excellent performance with mAP0.5 values of 94.8%, 98.6%, and 78.4% on HRSID, SSDD, and LS-SSDD-v1.0 datasets, respectively. Evaluation results demonstrate that CSS-YOLO outperforms state-of-the-art SAR ship detection approaches in accuracy and overall performance.