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http://dx.doi.org/10.9766/KIMST.2022.25.5.443

SAR Image Target Detection based on Attention YOLOv4  

Park, Jongmin (The Electrical Engineering of Korea Advanced Institute of Science and Technology)
Youk, Geunhyuk (The Electrical Engineering of Korea Advanced Institute of Science and Technology)
Kim, Munchurl (The Electrical Engineering of Korea Advanced Institute of Science and Technology)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.25, no.5, 2022 , pp. 443-461 More about this Journal
Abstract
Target Detection in synthetic aperture radar(SAR) image is critical for military and national defense. In this paper, we propose YOLOv4-Attention architecture which adds attention modules to YOLOv4 backbone architecture to complement the feature extraction ability for SAR target detection with high accuracy. For training and testing our framework, we present new SAR embedding datasets based on MSTAR SAR public datasets which are about poor environments for target detection such as various clutter, crowded objects, various object size, close to buildings, and weakness of signal-to-clutter ratio. Experiments show that our Attention YOLOv4 architecture outperforms original YOLOv4 architecture in SAR image target detection tasks in poor environments for target detection.
Keywords
Target Detection; Synthetic Aperture Radar; YOLOv4; Attention Algorithm; Deep Learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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