한국표면공학회지 (54권5호 278-284)
Corrosion Image Monitoring of steel plate by using k-means clustering
k-means 클러스터링을 이용한 강판의 부식 이미지 모니터링
김범수a, 권재성a, 최성웅a, 노정필b, 이경황c, 양정현a*
Beomsoo Kima, Jaesung Kwona, Sungwoong Choia, Jungpil Nohb, Kyunghwang Leec, Jeonghyeon Yanga*
a경상국립대학교 기계시스템공학과, b경상국립대학교 에너지기계공학과, c포스코 철강솔루션연구소
aDepartment of Mechanical System Engineering, Gyeongsang National University, Tongyeong, Gyeongnam, 53064, Korea bDepartment of Energy Mechanical Engineering, Gyeongsang National University, Tongyeong, Gyeongnam, 53064, Korea cSteel Solution R&D Center, POSCO, Inchen, 21985, Korea
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.
Corrosion, GrabCut Segmentation, Gaussian Mixture Model, HSV color space, k-means clustering
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