FEVT-SAR: Multicategory Oriented SAR Ship Detection Based on Feature Enhancement Vision Transformer

Issues such as complex noise interference and the long-tail distribution of data present many challenges to the multicategory ship detection task in synthetic aperture radar (SAR) images. This article proposes an efficient multicategory-oriented SAR ship detector, which adopts a powerful lightweight feature enhancement vision transformer (FEViT) backbone for a comprehensive feature representation in SAR ship images and, hence, is referred to as FEVT-SAR. FEViT includes two innovative lightweight modules: localized feature interactive convolution block (LFICB) and dual-granularity attention transformer block (DGTB). LFICB fuses multireceptive field local features to suppress speckle noise, while DGTB employs a coarse-to-fine self-attention to capture the global dependencies and avoids enormous computational costs. Moreover, a selective CopyPaste augmentation paradigm is designed to rebalance ship data distribution through data sampling. Finally, the performance of the FEVT-SAR is evaluated on two typical SAR ship datasets, namely SRSDD and HRSID. Experimental results show that the mean average precision 50 of FEVT-SAR reaches 68.59% and 89.62%, respectively. The proposed FEVT-SAR outperforms several state-of-the-art-oriented bounding box detectors in the multicategory ship dataset SRSDD while demonstrating its robustness in the single-category ship dataset HRSID.