Journal of the Korean institute of surface engineering (한국표면공학회지)

The Korean Institute of Surface Engineering (한국표면공학회)
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Corrosion Image Monitoring of steel plate by using k-means clustering

k-means 클러스터링을 이용한 강판의 부식 이미지 모니터링

  • Kim, Beomsoo (Department of Mechanical System Engineering, Gyeongsang National University) ;
  • Kwon, Jaesung (Department of Mechanical System Engineering, Gyeongsang National University) ;
  • Choi, Sungwoong (Department of Mechanical System Engineering, Gyeongsang National University) ;
  • Noh, Jungpil (Department of Energy Mechanical Engineering, Gyeongsang National University) ;
  • Lee, Kyunghwang (Steel Solution R&D Center, POSCO) ;
  • Yang, Jeonghyeon (Department of Mechanical System Engineering, Gyeongsang National University)
  • 김범수 (경상국립대학교 기계시스템공학과) ;
  • 권재성 (경상국립대학교 기계시스템공학과) ;
  • 최성웅 (경상국립대학교 기계시스템공학과) ;
  • 노정필 (경상국립대학교 에너지기계공학과) ;
  • 이경황 (포스코 철강솔루션연구소) ;
  • 양정현 (경상국립대학교 기계시스템공학과)
  • Received : 2021.10.28
  • Accepted : 2021.10.30
  • Published : 2021.10.31
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Abstract

Corrosion of steel plate is common phenomenon which results in the gradual destruction caused by a wide variety of environments. Corrosion monitoring is the tracking of the degradation progress for a long period of time. Corrosion on steel plate appears as a discoloration and any irregularities on the surface. In this study, we developed a quantitative evaluation method of the rust formed on steel plate by using k-means clustering from the corroded area in a given image. The k-means clustering for automated corrosion detection was based on the GrabCut segmentation and Gaussian mixture model(GMM). Image color of the corroded surface at cut-edge area was analyzed quantitatively based on HSV(Hue, Saturation, Value) color space.

Keywords

References

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