A New Gradient Based Surface Defect Detection Method for the Ceramic Tile

Year 2022, Volume: 26 Issue: 6, 1159 - 1169, 31.12.2022

Murat Alparslan Güngör

https://doi.org/10.16984/saufenbilder.1038022

Abstract

Ceramic tiles are controlled to detect surface defects after production because many defects may occur on their surface during production. The detection of ceramic tile surface defects is usually performed by human observations in most factories. In this paper, an image processing method was proposed to detect the defects. In the proposed method, first, the user selects the homogenous region in the image. Then the gradient-based image processing algorithm is applied. We conducted our study using simulated and real images to which we applied the conventional image processing methods and our proposed method. Performance of the proposed method was evaluated with quality metric and subjective evaluation. The obtained results demonstrate that the proposed method has very good performance and is very promising for ceramic tile application.

Keywords

Ceramic Tile, Surface Defect, Defect Detection, Image Processing

References

• [1] M. H. Karimi, D. Asemani, “Surface defect detection in tiling Industries using digital image processing methods: Analysis and evaluation”, ISA transactions, vol. 53, no. 3, pp. 834-844, 2014.
• [2] T. Czimmermann, G. Ciuti, M. Milazzo, M. Chiurazzi, S. Roccella, C. M. Oddo, P. Dario, “Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY”, Sensors, vol. 20, no. 5, pp. 1459, 2020.
• [3] G. M. Rahaman, M. Hossain, “Automatic defect detection and classification technique from image: a special case using ceramic tiles”, International Journal of Computer Science and Information Security, vol. 1, no. 1, pp. 22-30, 2009.
• [4] S. H. Hanzaei, A. Afshar, F. Barazandeh, “Automatic detection and classification of the ceramic tiles’ surface defects”, Pattern Recognition, vol. 66, pp. 174-189, 2017.
• [5] A. Mohan, S. Poobal, “Crack detection using image processing: A critical review and analysis”, Alexandria Engineering Journal, vol. 57, no. 2, pp. 787-798, 2018.
• [6] A. N. Shire, M. M. Khanapurkar, R. S. Mundewadikar, “Plain ceramic tiles surface defect detection using image processing”, in 2011 Fourth International Conference on Emerging Trends in Engineering & Technology, pp. 215-220, November 2011.
• [7] H. F. Ng, “Automatic thresholding for defect detection”, Pattern recognition letters, vol. 27, no. 14, pp. 1644-1649, 2006.
• [8] Z. Hocenski, T. Keser, A. Baumgartner, “A simple and efficient method for ceramic tile surface defects detection”, in 2007 IEEE International Symposium on Industrial Electronics, pp. 1606-1611, June 2007.
• [9] A. Sioma, “Automated Control of Surface Defects on Ceramic Tiles Using 3D Image Analysis”, Materials, vol. 13, no.5, pp. 1250, 2020.
• [10] A. Latif-Amet, A. Ertüzün, A. Erçil, “An efficient method for texture defect detection: sub-band domain co-occurrence matrices”, Image and Vision computing, vol. 18, no. 6-7, pp. 543-553, 2000.
• [11] S. Vasilic, Z. Hocenski, “The edge detecting methods in ceramic tiles defects detection”, in 2006 IEEE International Symposium on Industrial Electronics, pp. 469-472, July 2006.
• [12] Z. Hocenski, T. Keser, “Failure detection and isolation in ceramic tile edges based on contour descriptor analysis”, in 2007 Mediterranean Conference on Control & Automation, pp. 1-6, June 2007.
• [13] Y. C. Samarawickrama, C. D. Wickramasinghe, “Matlab based automated surface defect detection system for ceremic tiles using image processing”, in 2017 6th National Conference on Technology and Management (NCTM), pp. 34-39, January 2017.
• [14] Matlab, Image Processing Toolbox.
• [15] I. Sobel, “Camera models and perception”, Ph.D. thesis, Stanford University, CA, 1970.
• [16] J. Prewitt, “Object Enhancement and Extraction. Picture Processing and Psychopictorics”, NY, Academic Pres., 1970.
• [17] J. Canny, “A Computational approach to edge detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, pp. 679-700, 1986.
• [18] R. C. Gonzalez, R. E. Woods, S. L. Eddins, “Digital Image Processing Using MATLAB”, Saddle River, NJ, Pearson Prentice Hall., 2004.
• [19] S. Chowdhury, D. Dhara, S. Chowdhury, P. Haldar, K. Chatterjee, T. K. Bhattacharya, “A novel approach toward microstructure evaluation of sintered ceramic materials through image processing techniques”, International Journal of Applied Ceramic Technology, vol. 18, no. 3, pp. 773-780, 2021.