Defect Detection of Coatings on Metal Surfaces Based on K-Means Clustering Algorithm

Abstract

Defect detection is an important phase for the analysis of the surface quality of products as it influences the subsequent process. The existence of surface defects will affect the corrosion and wear resistance of the end product. The insufficient detection and classification rates of the standard algorithms are infeasible to accomplish the production requirements. Surface defect detection in coated metals with non-destructive techniques is an essential prerequisite for quality analysis in manufacturing stage. The existence of surface defects can significantly alter the deterioration resistance and instinctive qualities of a material and as a result more expansive analysis is essential. This paper proposes a competent and exact approach using K-means algorithm for the detection of surface coating defects. K-means is an unsupervised algorithm used for segmenting the area of interest from background. The proposed method uses a sequence of image processing algorithms to examine and validate the input image real-time accuracy for detection of defects. The proposed method efficiency is featured with test samples and results from experimental analysis. It shows that the proposed method be able to adequately and instinctively recognize the presence of defects inside coatings on metal surfaces.