Defect Detection of Coatings on Metal Surfaces Based on K-Means Clustering Algorithm 
Yasir Aslam1, Santhi N2, Ramasamy N3, K. Ramar4
1Yasir Aslam, Research Scholar, Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India.
2Santhi N, Associate Professor, Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India.
3Ramasamy N, Associate Professor, Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, India.
4K. Ramar, Professor & Dean, in Computing Science, Muthayammal Engineering College (autonomous), Rasipuram, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 5782-5786 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8557118419/2019©BEIESP | DOI: 10.35940/ijrte.D8557.118419
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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.
Keywords: Coated Surface, Image Segmentation, K-means Clustering, Surface Defect.
Scope of the Article: Frequency Selective Surface.