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Ship detection in synthetic aperture radar (SAR) images has been widely applied in maritime management and surveillance. However, some issues still exist in SAR ship detection due to the complex surroundings, scattering interferences, and diversity of the scales. To address these issues, an improved anchor-free method based on FCOS + ATSS is proposed for ship detection in SAR images. First, FCOS + ATSS is applied as the baseline to detect ships pixel by pixel, which can eliminate the effect of anchors and avoid missing detections. Then, an improved residual module (IRM) and a deformable convolution (Dconv) are embedded into the feature extraction network (FEN) to improve accuracy. Next, a joint representation of the classification score and localization quality is used to address the inconsistent classification and localization of the FCOS + ATSS network. Finally, the detection head is redesigned to improve positioning performance. Experimental simulation results show that the proposed method achieves 68.5% average precision (AP), which outperforms other methods, such as single shot multibox detector (SSD), faster region CNN (Faster R-CNN), RetinaNet, representative points (RepPoints), and FoveaBox. In addition, the proposed method achieves 60.8 frames per second (FPS), which meets the real-time requirement.

A Novel Anchor-Free Method Based on FCOS + ATSS for Ship Detection in SAR Images