Abstract
The research work of synthetic aperture radar (SAR) image target detection based on deep learning has made great progress. However, most of them apply the methods applicable to optical images directly to SAR images, ignoring the characteristics of targets in SAR images. For instance, the size of target in SAR images is usually small and volatile. Meanwhile, the target distribution is relatively sparse and the detection is affected by the complex background noise. In this paper, we propose an improved backbone network, called WAFormer, for ship targets detection in SAR images, based on the latest Swin-Transformer. WAFormer improves the local window attention mechanism of Swin-Transformer by introducing the new window settings. Our model can be more suitable to match the shape of the target, so that it obtains more accurate detection in SAR images. Experimental results show that the WAFormer achieves 74.4% mAP on the Official-SSDD SAR dataset, surpassing Swin-Transformer by +1.0, especially for large targets.
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Wang, Z., Wang, L., Wang, W., Tian, S., Zhang, Z. (2022). WAFormer: Ship Detection in SAR Images Based on Window-Aware Swin-Transformer. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_41
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