슈퍼픽셀 DBSCAN 군집 알고리즘을 이용한 용융아연도금 강판의 부식이미지 분석

Abstract

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 variousenvironmental 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 studydeveloped a quantitative evaluation method of the rust formed on GI steel plate using a superpixelbasedDBSCAN clustering method and k-means clustering from the corroded area in a given image. The superpixel-based DBSCAN clustering method decrease computational costs, reaching automaticsegmentation. 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 particularspatial region using their histograms.