k-means 클러스터링을 이용한 강판의 부식 이미지 모니터링

• Published in: Biuletyn Uniejowski Vol. 54; no. 5; pp. 278 - 284
• Main Authors: 김범수(Beomsoo Kim), 권재성(Jaesung Kwon), 최성웅(Sungwoong Choi), 노정필(Jungpil Noh), 이경황(Kyunghwang Lee), 양정현(Jeonghyeon Yang)
• Format: Journal Article
• Language: Korean
• Published: 한국표면공학회 2021
• Subjects: 기계공학; Gaussian Mixture Model; k-means clustering; Corrosion; GrabCut Segmentation; HSV color space
• ISSN: 1225-8024; 2299-8403; 2288-8403

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.