Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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SAR Image Target Detection based on Attention YOLOv4

어텐션 적용 YOLOv4 기반 SAR 영상 표적 탐지 및 인식

  • 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)
  • 박종민 (한국과학기술원 전기및전자공학부) ;
  • 육근혁 (한국과학기술원 전기및전자공학부) ;
  • 김문철 (한국과학기술원 전기및전자공학부)
  • Received : 2022.05.06
  • Accepted : 2022.09.16
  • Published : 2022.10.05
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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

Acknowledgement

본 연구는 국방과학연구소의 연구비 지원으로 수행되었습니다.

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