Abstract
Ship detection in large-scale synthetic aperture radar (SAR) images has achieved breakthroughs as a result of the improvement of SAR imaging technology. However, there still exist some issues due to the scattering interference, sparsity of ships, and dim and small ships. To address these issues, an anchor-free method is proposed for dim and small ship detection in large-scale SAR images. First, fully convolutional one-stage object detection (FCOS) as the baseline is applied to detecting ships pixel by pixel, which can eliminate the effect of anchors and avoid the missing detection of small ships. Then, considering the particularity of SAR ships, the sample definition is redesigned based on the statistical characteristics of ships. Next, the feature extraction is redesigned to improve the feature representation for dim and small ships. Finally, the classification and regression are redesigned by introducing an improved focal loss and regression refinement with complete intersection over union (CIoU) loss. Experimental simulation results show that the proposed R-FCOS method can detect dim and small ships in large-scale SAR images with higher accuracy compared with other methods.
Highlights
Due to the continuous improvements in the quantity and quality of synthetic aperture radar (SAR) images [1–3], ship detection has been widely applied for maritime management and surveillance
Li et al [9] introduced the superpixels into constant false alarm rate (CFAR) ship detection method
The R-fully convolutional one-stage object detection (FCOS) method, including redesigned sample definition, feature extraction, classification, and regression are described in detail
Summary
Due to the continuous improvements in the quantity and quality of synthetic aperture radar (SAR) images [1–3], ship detection has been widely applied for maritime management and surveillance. Li et al [9] introduced the superpixels into constant false alarm rate (CFAR) ship detection method. Salembier et al [10] applied graph signal processing based on Maxtree representation for ship detection. Lin et al [11] proposed a ship detection method via superpixels and fisher vectors. Wang et al [12] proposed the local contrast of fisher vectors (LCFVs) for detecting ships. The time cost is high due to the complex detection process
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