A Lightweight Arbitrarily Oriented Detector Based on Transformers and Deformable Features for Ship Detection in SAR Images
Bingji Chen,
Fengli Xue,
Hongjun Song
Abstract:Lightweight ship detection is an important application of synthetic aperture radar (SAR). The prevailing trend in recent research involves employing a detection framework based on convolutional neural networks (CNNs) and horizontal bounding boxes (HBBs). However, CNNs with local receptive fields fall short in acquiring adequate contextual information and exhibit sensitivity to noise. Moreover, HBBs introduce significant interference from both the background and adjacent ships. To overcome these limitations, th… Show more
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