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

  • 제20권6호
  • /
  • Pages.758-766
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  • 2017
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  • 1598-9127(pISSN)
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  • 2636-0640(eISSN)
한국군사과학기술학회 (The Korea Institute of Military Science and Technology)
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적외선 영상에서의 표적과 클러터 구분을 위한 Hybrid Machine Character 기반의 Du-CNN 설계

A Design of Du-CNN based on the Hybrid Machine Characters to Classify Target and Clutter in The IR Image

  • 이주영 (국방과학연구소 제3기술연구본부) ;
  • 임재완 (국방과학연구소 제3기술연구본부) ;
  • 백하은 (국방과학연구소 제3기술연구본부) ;
  • 김춘호 (국방과학연구소 제1기술연구본부) ;
  • 박정수 (국방과학연구소 제1기술연구본부) ;
  • 고은진 (국방과학연구소 제3기술연구본부)
  • Lee, Juyoung (The 3rd Research and Development Institute, Agency for Defense Development) ;
  • Lim, Jaewan (The 3rd Research and Development Institute, Agency for Defense Development) ;
  • Baek, Haeun (The 3rd Research and Development Institute, Agency for Defense Development) ;
  • Kim, Chunho (The 1st Research and Development Institute, Agency for Defense Development) ;
  • Park, Jungsoo (The 1st Research and Development Institute, Agency for Defense Development) ;
  • Koh, Eunjin (The 3rd Research and Development Institute, Agency for Defense Development)
  • 투고 : 2017.04.20
  • 심사 : 2017.10.27
  • 발행 : 2017.12.05
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초록

In this paper, we propose a robust duality of CNN(Du-CNN) method which can classify the target and clutter in coastal environment for IR Imaging Sensor. In coastal environment, there are various clutter that have many similarities with real target due to diverse change of air temperature, water temperature, weather and season. Also, real target have various feature due to the same reason. Thus, the proposed Du-CNN method adopts human's multiple personality utilization and CNN technique to learn and classify target and clutter. This method has an advantage of the real time operation. Experimental results on sampled dataset of real infrared target and clutter demonstrate that the proposed method have better success rate to classify the target and clutter than general CNN method.

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참고문헌

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