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

  • 제27권6호
  • /
  • Pages.665-676
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  • 2024
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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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Grad-CAM 기반의 설명가능한 인공지능을 사용한 합성 이미지 개선 방법

Improving Confidence in Synthetic Infrared Image Refinement using Grad-CAM-based Explainable AI Techniques

  • 김태호 (한국과학기술원 기계기술연구소) ;
  • 김강산 (한국과학기술원 항공우주공학과) ;
  • 방효충 (한국과학기술원 항공우주공학과)
  • Taeho Kim (Mechanical Engineering Research Institute, KAIST) ;
  • Kangsan Kim (Department of Aerospace Engineering, KAIST) ;
  • Hyochoong Bang (Department of Aerospace Engineering, KAIST)
  • 투고 : 2024.07.10
  • 심사 : 2024.09.13
  • 발행 : 2024.12.05
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초록

Infrared imaging is a powerful non-destructive and non-invasive technique to detect infrared radiations and capture valuable insights inaccessible through the visible spectrum. It has been widely used in the military for reconnaissance, hazard detection, night vision, guidance systems, and countermeasures. There is a huge potential for machine learning models to improve trivial infrared imaging tasks in military applications. One major roadblock is the scarcity and control over infrared imaging datasets related to military applications. One possible solution is to use synthetic infrared images to train machine learning networks. However, synthetic IR images present a domain gap and produce weak learning models that do not generalize well. We investigate adversarial networks and Explainable AI(XAI) techniques to refine synthetic infrared imaging data, enhance their realism, and synthesize refiner networks with XAI. We use a U-Net-based refiner network to refine synthetic infrared data and a PatchGAN discriminator to distinguish between the refined and real IR images. Grad-CAM XAI technique is used for network synthesis. We also analyzed the frequency domain patterns and power spectra of real and synthetic infrared images to find key attributes to distinguish real from synthetic. We tested our refined images on the realism benchmarks using frequency domain analysis.

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과제정보

본 논문은 국방과학연구소를 통해 인공지능 비행제어 특화연구실 산하 인공지능 기반 합성 센서 영상 분석 및 개선기법 연구(IC-05) 관련 지원을 받아 수행되었음(UD230014SD).

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