Articles

All articles  |  Recent articles

Deep Learning for Ceramic Surface Defect Inspection: A State-of-the-Art Survey

B.S.Wang¹, L. Zhang²

¹ School of Computer and Software, Nanyang Institute of Technology, Nanyang, 473004, China.
² Changchun Oubang Biotechnology Co. Ltd., Changchun, 130000, China.

received April 14, 2025, received in revised form May 11, 2025, accepted May 20, 2025

Vol. 16, No. 4, Pages 205-218   DOI: 10.4416/JCST2025-00011

Abstract

Surface defect detection in ceramic manufacturing is crucial for ensuring product quality and functional performance. This comprehensive review examines the evolution and current state of deep learning applications in ceramic surface inspection. Traditional inspection methods, including manual visual inspection and conventional image processing techniques, face limitations in handling complex textures, varying lighting conditions, and subtle defect patterns. The review analyzes various deep learning architectures, from basic Convolutional Neural Networks (CNNs) to sophisticated region-based networks and segmentation models, highlighting their strengths and limitations in ceramic defect detection. Implementation considerations, including data acquisition, preprocessing strategies, model training approaches, and real-time processing requirements, are discussed extensively. The paper addresses critical challenges such as limited labeled data availability, class imbalance, and the need for robust performance under varying production conditions. Recent innovations in network architectures, training methodologies, and deployment strategies that address industry-specific challenges are examined. The review provides valuable insights for both researchers and practitioners, bridging the gap between academic research and industrial implementation while identifying emerging trends and future research directions in this rapidly evolving field.

Keywords

Automated inspection, surface quality control, machine vision, neural networks, manufacturing automation

References

¹ Zhao, Z.: Review of non-destructive testing methods for defect detection of ceramics, Ceram. Int., 47, 4389 – 4397, (2021). doi: https://doi.org/10.1016/j.ceramint.2020.10.065

² Huang, C.-Y., Lin, I.-C., Liu, Y.-L.: Applying deep learning to construct a defect detection system for ceramic substrates, Appl. Sci., 12, 2269, (2022). doi: https://doi.org/10.3390/app12052269

³ Dong, G., Sun, S., Wang, Z., Wu, N., Huang, P., Feng, H., Pan, M.: Application of machine vision-based NDT technology in ceramic surface defect detection – a review, Mater. Test., 64, 202 – 219, (2022). doi: https://doi.org/10.1515/mt-2021-2012

⁴ Kesharaju, M., Nagarajah, R., Zhang, T., Crouch, I.: Ultrasonic sensor based defect detection and characterisation of ceramics, Ultrasonics, 54, 312 – 317, (2014). doi: https://doi.org/10.1016/j.ultras.2013.07.018

⁵ 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, 101692, (2022). doi: https://doi.org/10.1016/j.aei.2022.101692

⁶ Wan, G., Fang, H., Wang, D., Yan, J., Xie, B.: Ceramic tile surface defect detection based on deep learning, Ceram. Int., 48, 11085 – 11093, (2022). doi: https://doi.org/10.1016/j.ceramint.2021.12.328

⁷ Hanzaei, S.H., Afshar, A., Barazandeh, F.: Automatic detection and classification of the ceramic tiles' surface defects, Pattern Recognit., 66, 174 – 189, (2017). doi: https://doi.org/10.1016/j.patcog.2016.11.021

⁸ Chen, W., Zou, B., Huang, C., Yang, J., Li, L., Liu, J., Wang, X.: The defect detection of 3D-printed ceramic curved surface parts with low contrast based on deep learning, Ceram. Int., 49, 2881 – 2893, (2023). doi: https://doi.org/10.1016/j.ceramint.2022.09.272

⁹ Cumbajin, E., Rodrigues, N., Costa, P., Miragaia, R., Frazão, L., Costa, N., Fernández-Caballero, A., Carneiro, J., Buruberri, L.H., Pereira, A.: A real-time automated defect detection system for ceramic pieces manufacturing process based on computer vision with deep learning, Sensors, 24, 232, (2024). doi: https://doi.org/10.3390/s24010232

¹⁰ Fang, T., Jafari, M.A., Danforth, S.C., Safari, A.: Signature analysis and defect detection in layered manufacturing of ceramic sensors and actuators, Mach. Vis. Appl., 15, 63 – 75, (2003). doi: https://doi.org/10.1007/s00138-002-0074-1

¹¹ Sun, P., Hua, C., Ding, W., Hua, C., Liu, P., Lei, Z.: Ceramic tableware surface defect detection based on deep learning, Eng. Appl. Artif. Intell., 141, 109723, (2025). doi: https://doi.org/10.1016/j.engappai.2024.109723

¹² Cao, T., Song, K., Xu, L., Feng, H., Yan, Y., Guo, J.: Balanced multi-scale target score network for ceramic tile surface defect detection, Measurement, 224, 113914, (2024). doi: https://doi.org/10.1016/j.measurement.2023.113914

¹³ Chen, W., Zou, B., Yang, G., Zheng, Q., Lei, T., Huang, C., Liu, J., Li, L.: A real-time detection system for multiscale surface defects of 3D printed ceramic parts based on deep learning, Ceram. Int., 50, 13101 – 13112, (2024). doi: https://doi.org/10.1016/j.ceramint.2024.01.220

¹⁴ Zhang, H., Peng, L., Yu, S., Qu, W.: Detection of surface defects in ceramic tiles with complex texture, IEEE Access., 9, 92788 – 92797, (2021). doi: https://doi.org/10.1109/ACCESS.2021.3093090

¹⁵ Zhou, J., Li, H., Lu, L., Cheng, Y.: Machine vision-based surface defect detection study for ceramic 3D printing, Machines, 12, 166, (2024). doi: https://doi.org/10.3390/machines12030166

¹⁶ Liu, C.H., Jeyaprakash, N., Yang, C.H.: Material characterization and defect detection of additively manufactured ceramic teeth using non-destructive techniques, Ceram. Int., 47, 7017 – 7031, (2021). doi: https://doi.org/10.1016/j.ceramint.2020.11.052

¹⁷ Mariyadi, B., Fitriyani, N., Sahroni, T.R.: 2D detection model of defect on the surface of ceramic tile by an artificial neural network, J. Phys. Conf. Ser., 1764, 012176, (2021). doi: https://doi.org/10.1088/1742-6596/1764/1/012176

¹⁸ Lu, F., Zhang, Z., Guo, L., Chen, J., Zhu, Y., Yan, K., Zhou, X.: HFENet: A lightweight hand-crafted feature enhanced CNN for ceramic tile surface defect detection, Int. J. Intell. Syst., 37, 10670 – 10693, (2022). doi: https://doi.org/10.1002/int.22935

¹⁹ Cumbajin, E., Rodrigues, N., Costa, P., Miragaia, R., Frazão, L., Costa, N., Fernández-Caballero, A., Carneiro, J., Buruberri, L.H., Pereira, A.: A systematic review on deep learning with CNNs applied to surface defect detection, J. Imaging., 9, 193, (2023). doi: https://doi.org/10.3390/jimaging9100193

²⁰ Stephen, O., Maduh, U.J., Sain, M.: A machine learning method for detection of surface defects on ceramic tiles using convolutional neural networks, Electron., 11, 55, (2022). doi: https://doi.org/10.3390/electronics11010055

²¹ Huang, Y.P., Su, C.M., Basanta, H., Tsai, Y.L.: Imbalance modelling for defect detection in ceramic substrate by using convolutional neural network, Processes., 9, 1678, (2021). doi: https://doi.org/10.3390/pr9091678

²² Nogay, H.S., Akinci, T.C., Yilmaz, M.: Detection of invisible cracks in ceramic materials using pre-trained deep convolutional neural network, Neural Comput. Appl., 34, 1423 – 1432, (2022). doi: https://doi.org/10.1007/s00521-021-06652-w

²³ Zou, M., Matsunaga, K., Ueda, Y., Sugawara, T., Osanai, H., Kageyama, Y.: Defect detection in metal-ceramic substrate based on image processing and machine learning, IEEJ J. Ind. Appl., 23006878, (2024).

²⁴ Song, K., Yan, Y.: A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects, Appl. Surf. Sci., 285, 858 – 864, (2013). doi: https://doi.org/10.1016/j.apsusc.2013.09.002

²⁵ Ghorai, S., Mukherjee, A., Gangadaran, M., Dutta, P.K.: Automatic defect detection on hot-rolled flat steel products, IEEE Trans. Instrum. Meas., 62, 612 – 621, (2012).

²⁶ Li, J., Su, Z., Geng, J., Yin, Y.: Real-time detection of steel strip surface defects based on improved YOLO detection network, IFAC-Pap. OnLine, 51, 76 – 81, (2018). doi: https://doi.org/10.1016/j.ifacol.2018.09.412

²⁷ Ren, Z., Fang, F., Yan, N., Wu, Y.: State of the art in defect detection based on machine vision, Int. J. Precis. Eng. Manuf. Green Technol., 9, 661 – 691, (2022). doi: https://doi.org/10.1007/s40684-021-00343-6

²⁸ Xie, X.: A review of recent advances in surface defect detection using texture analysis techniques, ELCVIA Electron. Lett. Comput. Vis. Image Anal., 1 – 22 (2008).

²⁹ Iivarinen, J., Rauhamaa, J., Visa, A.: Unsupervised segmentation of surface defects. In: Proc. 13th Int. Conf. Pattern Recognit., 4, 356 – 360 (1996). doi: https://doi.org/10.1109/ICPR.1996.547445

³⁰ Mäenpää, T., Pietikäinen, M.: Texture analysis with local binary patterns. In: Handbook of Pattern Recognition and Computer Vision. World Scientific, 197 – 216, (2005).

³¹ Huang, Y., Chan, K.L.: Texture decomposition by harmonics extraction from higher order statistics, IEEE Trans. Image Process., 13, 1 – 14, (2004). doi: https://doi.org/10.1109/TIP.2003.819432

³² Ng, H.F.: Automatic thresholding for defect detection, Pattern Recognit. Lett., 27, 1644 – 1649, (2006). doi: https://doi.org/10.1016/j.patrec.2006.03.009

³³ Latif-Amet, A., Ertüzün, A., Erçil, A.: An efficient method for texture defect detection: sub-band domain co-occurrence matrices, Image Vis. Comput., 18, 543 – 553, (2000). doi: https://doi.org/10.1016/S0262-8856(99)00062-1

³⁴ Mahmoudi, M., Ezzat, A.A., Elwany, A.: Layerwise anomaly detection in laser powder-bed fusion metal additive manufacturing, J. Manuf. Sci. Eng., 141, 031002, (2019).

³⁵ Khanzadeh, M., Chowdhury, S., Marufuzzaman, M., Tschopp, M.A., Bian, L.: Porosity prediction: supervised-learning of thermal history for direct laser deposition, J. Manuf. Syst., 47, 69 – 82, (2018). doi: https://doi.org/10.1016/j.jmsy.2018.04.001

³⁶ Gobert, C., Reutzel, E.W., Petrich, J., Nassar, A.R., Phoha, S.: Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging, Addit. Manuf., 21, 517 – 528, (2018). doi: https://doi.org/10.1016/j.addma.2018.04.005

³⁷ García-Moreno, A.-I., Alvarado-Orozco, J.-M., Ibarra-Medina, J., Martínez-Franco, E.: Image-based porosity classification in Al-alloys by laser metal deposition using random forests, Int. J. Adv. Manuf. Technol., 110, 2827 – 2845, (2020). doi: https://doi.org/10.1007/s00170-020-05887-6

³⁸ Montazeri, M., Nassar, A.R., Dunbar, A.J., Rao, P.: In-process monitoring of porosity in additive manufacturing using optical emission spectroscopy, IISE Trans., 52, 500 – 515, (2020).

³⁹ Min, B., Tin, H., Nasridinov, A., Yoo, K.-H.: Abnormal detection and classification in i-ceramic images. In: 2020 IEEE Int. Conf. Big Data Smart Comput. (BigComp), 17 – 18, (2020). doi: https://doi.org/10.1109/BigComp48618.2020.0-106

⁴⁰ Karangwa, J., Kong, L., You, T., Zheng, J.: Automated surface defects detection on mirrorlike materials by using Faster R-CNN. In: 2020 7th Int. Conf. Inf. Sci. Control Eng. (ICISCE), 2288 – 2294, (2020). doi: https://doi.org/10.1109/ICISCE50968.2020.00341

⁴¹ Birlutiu, A., Burlacu, A., Kadar, M., Onita, D.: Defect Detection in Porcelain Industry Based on Deep Learning Techniques. 2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 263 – 270, (2017). doi: https://doi.org/10.1109/SYNASC.2017.00049

⁴² Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell., 39, 1137 – 1149, (2017). doi: https://doi.org/10.1109/TPAMI.2016.2577031

⁴³ Wang, Y., Liu, M., Zheng, P., Yang, H., Zou, J.: A smart surface inspection system using faster R-CNN in cloud-edge computing environment, Adv. Eng. Inform., 43, 101037, (2020). doi: https://doi.org/10.1016/j.aei.2020.101037

⁴⁴ Cha, Y.J., Choi, W., Suh, G., Mahmoudkhani, S., Büyüköz­türk, O.: Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types, Comput.-Aided Civ. Infrastruct. Eng., 33, 731 – 747, (2018). doi: https://doi.org/10.1111/mice.12334

⁴⁵ Zhou, X., Wang, Y., Xiao, C., Zhu, Q., Lu, X., Zhang, H., Ge, J., Zhao, H.: Automated visual inspection of glass bottle bottom with saliency detection and template matching, IEEE Trans. Instrum. Meas., 68, 4253 – 4267, (2019). doi: https://doi.org/10.1109/TIM.2018.2886977

⁴⁶ Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., Yang, X.: Automatic pixel-level crack detection and measurement using fully convolutional network, Comput.-Aided Civ. Infrastruct. Eng., 33, 1090 – 1109, (2018). doi: https://doi.org/10.1111/mice.12412

⁴⁷ Liu, W., Chen, J., Liu, C., Ban, X., Ma, B., Wang, H., Xue, W., Guo, Y.: Boundary learning by using weighted propagation in convolution network, J. Comput. Sci., 62, 101709, (2022). doi: https://doi.org/10.1016/j.jocs.2022.101709

⁴⁸ Junior, G.S., Ferreira, J., Millán-Arias, C., Daniel, R., Junior, A.C., Fernandes, B.J.T.: Ceramic cracks segmentation with deep learning, Appl. Sci., 11, 6017, (2021). doi: https://doi.org/10.3390/app11136017

⁴⁹ Howard, A.G.: Mobilenets: Efficient convolutional neural networks for mobile vision applications, arXiv Prepr. arXiv, 1704.04861, (2017).

Copyright

Göller Verlag GmbH