Efficient surface crack detection in ceramic tiles using MATLAB image processing

Year 2025, Volume: 67 Issue: 1, 12 - 26

Merve Ozkan-okay , Ömer Ahat

https://doi.org/10.33769/aupse.1498129

Abstract

The significance of quality control in the ceramic tile industry cannot be overstated. Traditional approaches to quality control in this sector have relied heavily on labor-intensive manual inspection methods, which are not only costly but also less efficient. Moreover, these methods often fall short in terms of accuracy, primarily due to the challenging industrial environment and the potential for human error. To address these shortcomings, this research proposed an innovative automated inspection system utilizing advanced image processing techniques specifically designed for the ceramic tile industry. This system detects defects such as corner damage, edge damage and center cracks on tile surfaces and provides information for quality assurance processes by classifying tile quality through rigorous analysis and comparison of various quality parameters. The performance of the system is tested with a total of 120 synthetic data, including those with cracks, damaged corners and discoloration. As a result of the testing process, a 97.5% accuracy rate is obtained. Furthermore, the system operates with a processing time of approximately 0.8 seconds per piece. This study, which offers both high accuracy and efficiency, promises significant improvements in quality control processes by offering an alternative to manual inspection methods.

Keywords

Ceramic tile defect, image processing, machine learning, grid search algorithm

References

• Lu, Q., Lin, J., Luo, L., Zhang, Y., Zhu, W., A supervised approach for automated surface defect detection in ceramic tile quality control, Adv. Eng. Inform., 53 (2022), 101692, https://doi.org/10.1016/j.aei.2022.101692.
• Hocenski, Z., Matic, T., Vidovic, I., Technology transfer of computer vision defect detection to ceramic tiles industry, 2016 International Conference on Smart Systems and Technologies (SST), (2016), 301-305, https://doi.org/10.1109/SST.2016.7765678.
• Mansoory, M. S., Tajik, H., Mohamadi, G., Pashna, M., Edge defect detection in ceramic tile based on boundary analysis using fuzzy thresholding and radon transform, 2008 International Symposium on Signal Processing and Information Technology, (2008), 58-62, https://doi.org/10.1109/ISSPIT.2008.4775686.
• Younas, M., Nawaz, Q., Hamid, I., Gilani, S. M. M., Javed Iqbal, M., A novel approach of ceramic tile crack detection using morphological operations, Mehran University Research Journal of Engineering Technology, 41 (2) (2022), 146-154, https://doi.org/10.22581/muet1982.2202.14.
• Samarawickrama, Y. C., Wickramasinghe, C. D., Matlab based automated surface defect detection system for ceremic tiles using image processing, 2017 6th National Conference on Technology and Management (NCTM), (2017), 34-39, https://doi.org/10.1109/NCTM.2017.7872824.
• Hanzaei, S. H., Afshar, A., Barazandeh, F., Automatic detection and classification of the ceramic tiles’ surface defects, Pattern Recog., 66 (2017), 174-189, https://doi.org/10.1016/j.patcog.2016.11.021.
• Iglesias, C., Martínez, J., Taboada, J., Automated vision system for quality inspection of slate slabs, Comput. Ind., 99 (2018), 119-129, https://doi.org/10.1016/j.mcm.2011.11.016.
• Martínez, J., López, M., Matías, J. M., Taboada, J., Classifying slate tile quality using automated learning techniques, Math. Comput. Model., 57 (7-8) (2013), 1716-1721.
• Wan, G., Fang, H., Wang, D., Yan, J., Xie, B., Ceramic tile surface defect detection based on deep learning, Ceram. Int., 48 (8) (2022), 11085-11093, https://doi.org/10.1016/j.ceramint.2021.12.328.
• Stephen, O., Maduh, U. J., Sain, M., A machine learning method for detection of surface defects on ceramic tiles using convolutional neural networks, Electronics, 11 (1) (2021), 55, https://doi.org/10.3390/electronics11010055.
• Najafabadi, F. S., Pourghassem, H., Corner defect detection based on dot product in ceramic tile images, 7th International Colloquium on Signal Processing and its Applications, (2011), 293-297, https://doi.org/10.1109/CSPA.2011.5759890.
• Xie, J., Zhang, J., Liang, F., Yang, Y., Xu, X., Dong, J., GSPSO-LRF-ELM: grid search and particle swarm optimization-based local receptive field-enabled extreme learning machine for surface defects detection and classification on the magnetic tiles, Discret. Dyn. Nat. Soc., (2020), 1-10, https://doi.org/10.1155/2020/4565769.
• Saberironaghi, A., Ren, J., El-Gindy, M., Defect detection methods for industrial products using deep learning techniques: a review, Algorithms, 16 (2) (2023), 95, https://doi.org/10.3390/a16020095.