Korean Institute of Surface Engineering

pISSN : 1225-8024 | eISSN : 3399-8403


한국표면공학회지 (55권3호 164-172)

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

슈퍼픽셀 DBSCAN 군집 알고리즘을 이용한 용융아연도금 강판의 부식이미지 분석

김범수a, 김연원b, 이경황c, 양정현a*
Beomsoo Kima, Yeonwon Kimb, Kyunghwang Leec, Jeonghyeon Yanga*

a경상국립대학교 기계시스템공학과, b목포해양대학교 메카트로닉스공학부, c포스코 철강솔루션연구소
aDepartment of Mechanical System Engineering, Gyeongsang National University, Tongyeong, 53064, Korea bDivision of Mechatronics Engineering, Mokpo National Maritime University, Mokpo, 58628, Korea cSteel Solution R&D Center, POSCO, Inchen, 21985, Korea

DOI : 10.5695/JSSE.2022.55.3.164


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 superpixelbased 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.


Corrosion; Superpixel; DBSCAN; k-means clustering; HSV color space.