한국표면공학회지 (Journal of the Korean institute of surface engineering)

  • 제55권3호
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  • Pages.164-172
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  • 2022
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  • 1225-8024(pISSN)
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  • 2288-8403(eISSN)
한국표면공학회 (The Korean Institute of Surface Engineering)
권호 표지
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슈퍼픽셀 DBSCAN 군집 알고리즘을 이용한 용융아연도금 강판의 부식이미지 분석

Corrosion image analysis on galvanized steel by using superpixel DBSCAN clustering algorithm

  • 김범수 (경상국립대학교 기계시스템공학과) ;
  • 김연원 (목포해양대학교 메카트로닉스공학부) ;
  • 이경황 (포스코 철강솔루션연구소) ;
  • 양정현 (경상국립대학교 기계시스템공학과)
  • Kim, Beomsoo (Department of Mechanical System Engineering, Gyeongsang National University) ;
  • Kim, Yeonwon (Division of Mechatronics Engineering, Mokpo National Maritime University) ;
  • Lee, Kyunghwang (Steel Solution R&D Center, POSCO) ;
  • Yang, Jeonghyeon (Department of Mechanical System Engineering, Gyeongsang National University)
  • 투고 : 2022.06.15
  • 심사 : 2022.06.24
  • 발행 : 2022.06.30
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초록

Hot-dip galvanized steel(GI) is widely used throughout the industry as a corrosion resistance material. Corrosion of steel is a common phenomenon that results in the gradual degradation under various environmental conditions. Corrosion monitoring is to track the degradation progress for a long time. Corrosion on steel plate appears as discoloration and any irregularities on the surface. This study developed a quantitative evaluation method of the rust formed on GI steel plate using a superpixel-based DBSCAN clustering method and k-means clustering from the corroded area in a given image. The superpixel-based DBSCAN clustering method decrease computational costs, reaching automatic segmentation. The image color of the rusty surface was analyzed quantitatively based on HSV(Hue, Saturation, Value) color space. In addition, two segmentation methods are compared for the particular spatial region using their histograms.

키워드

참고문헌

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