k-means 클러스터링을 이용한 강판의 부식 이미지 모니터링

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 t...

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Published inBiuletyn 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
LanguageKorean
Published 한국표면공학회 2021
Subjects
기계공학
Gaussian Mixture Model
k-means clustering
Corrosion
GrabCut Segmentation
HSV color space
Online AccessGet full text
ISSN1225-8024
2299-8403
2288-8403

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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.
AbstractList 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. KCI Citation Count: 4
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.
Author 양정현(Jeonghyeon Yang)
권재성(Jaesung Kwon)
노정필(Jungpil Noh)
김범수(Beomsoo Kim)
최성웅(Sungwoong Choi)
이경황(Kyunghwang Lee)
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  fullname: 이경황(Kyunghwang Lee)
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  fullname: 양정현(Jeonghyeon Yang)
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DocumentTitleAlternate Corrosion Image Monitoring of steel plate by using k-means clustering
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Keywords Gaussian Mixture Model
k-means clustering
Corrosion
GrabCut Segmentation
HSV color space
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Title k-means 클러스터링을 이용한 강판의 부식 이미지 모니터링
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