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

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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Deep Learning based Mine Detection using Side-Scan Sonar Image

측면주사 소나 영상을 이용한 딥러닝 기반 기뢰 탐지

  • Jonghyeon Mun (Department of Electronics and Communications Engineering, Kwangwoon University) ;
  • Sieon Park (Department of Electronics and Communications Engineering, Kwangwoon University) ;
  • Jaehwan Kim (Department of Defense Acquisition Program, Kwangwoon University) ;
  • Changhyung Kim (Jstackup Co., Ltd.) ;
  • Daeyeol Kim (Department of Artificial Intelligence, Kyungnam University) ;
  • Chaebong Sohn (Department of Electronics and Communications Engineering, Kwangwoon University)
  • 문종현 (광운대학교 전자통신공학과) ;
  • 박시언 (광운대학교 전자통신공학과) ;
  • 김재환 (광운대학교 방위사업학과) ;
  • 김창형 (주식회사 제이스택업) ;
  • 김대열 (경남대학교 인공지능학과) ;
  • 손채봉 (광운대학교 전자통신공학과)
  • Received : 2024.06.18
  • Accepted : 2025.02.21
  • Published : 2025.04.05
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Abstract

This paper compares and analyzes the performance of deep learning-based object detection models for mine detection, particularly Faster R-CNN and YOLOv5, using side-scan sonar images. Additionally, it proposes effective data augmentation method to enhance the generalization performance of mine detection models. Performance evaluation based on the structure and size of each model indicates that the two-stage model, Faster R-CNN, is more suitable for precise search tasks, while the one-stage model, YOLOv5, offers faster processing speed, making it advantageous for rapid mine detection in large maritime areas. This contributes significantly to improving the efficiency of maritime boundary missions and operations utilizing autonomous underwater vehicles, thereby making a substantial impact on naval operations and mine counter measures strategies.

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