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While there are various methods that can be used for inspecting surfaces, such as those of metal and building materials, there are only a limited number of techniques that are specifically designed to analyze specialized surfaces, such as ceramics, which can potentially reveal distinctive anomalies or characteristics that require a more precise and focused approach. This article describes a study and proposes an extended solution for defect detection on ceramic pieces within an industrial environment, utilizing a computer vision system with deep learning models. The solution includes an image acquisition process and a labeling platform to create training datasets, as well as an image preprocessing technique, to feed a machine learning algorithm based on convolutional neural networks (CNNs) capable of running in real time within a manufacturing environment. The developed solution was implemented and evaluated at a leading Portuguese company that specializes in the manufacturing of tableware and fine stoneware. The collaboration between the research team and the company resulted in the development of an automated and effective system for detecting defects in ceramic pieces, achieving an accuracy of 98.00% and an F1-Score of 97.29%.<\/jats:p>","DOI":"10.3390\/s24010232","type":"journal-article","created":{"date-parts":[[2023,12,31]],"date-time":"2023-12-31T06:00:21Z","timestamp":1704002421000},"page":"232","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Real-Time Automated Defect Detection System for Ceramic Pieces Manufacturing Process Based on Computer Vision with Deep Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2780-6210","authenticated-orcid":false,"given":"Esteban","family":"Cumbajin","sequence":"first","affiliation":[{"name":"Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0953-6018","authenticated-orcid":false,"given":"Nuno","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal"}]},{"given":"Paulo","family":"Costa","sequence":"additional","affiliation":[{"name":"Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4213-9302","authenticated-orcid":false,"given":"Rolando","family":"Miragaia","sequence":"additional","affiliation":[{"name":"Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2571-7940","authenticated-orcid":false,"given":"Lu\u00eds","family":"Fraz\u00e3o","sequence":"additional","affiliation":[{"name":"Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2353-369X","authenticated-orcid":false,"given":"Nuno","family":"Costa","sequence":"additional","affiliation":[{"name":"Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8211-0398","authenticated-orcid":false,"given":"Antonio","family":"Fern\u00e1ndez-Caballero","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n en Inform\u00e1tica de Albacete, 02071 Albacete, Spain"},{"name":"Departamento de Sistemas Inform\u00e1ticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain"}]},{"given":"Jorge","family":"Carneiro","sequence":"additional","affiliation":[{"name":"Grestel-Produtos Cer\u00e2micos S.A, Zona Industrial de Vagos-Lote 78, 3840-385 Vagos, Portugal"}]},{"given":"Leire H.","family":"Buruberri","sequence":"additional","affiliation":[{"name":"Grestel-Produtos Cer\u00e2micos S.A, Zona Industrial de Vagos-Lote 78, 3840-385 Vagos, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5062-1241","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Pereira","sequence":"additional","affiliation":[{"name":"Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, Portugal"},{"name":"INOV INESC Inova\u00e7\u00e3o, Institute of New Technologies, Leiria Office, 2411-901 Leiria, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Birlutiu, A., Burlacu, A., Kadar, M., and Onita, D. (2017, January 21\u201324). Defect detection in porcelain industry based on deep learning techniques. Proceedings of the 2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2017, Timisoara, Romania.","DOI":"10.1109\/SYNASC.2017.00049"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kou, X., He, Y., and Qian, Y. (2021, January 22\u201324). An improvement and application of a model conducive to productivity optimization. Proceedings of the 2021 IEEE International Conference on Power Electronics, Computer Applications, ICPECA 2021, Shenyang, China.","DOI":"10.1109\/ICPECA51329.2021.9362627"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"040801","DOI":"10.1115\/1.4049535","article-title":"Image-Based Surface Defect Detection Using Deep Learning: A Review","volume":"21","author":"Bhatt","year":"2021","journal-title":"J. Comput. Inf. Sci. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Prakash, N., Manconi, A., and Loew, S. (2020). Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-11876"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cumbajin, E., Rodrigues, N., Costa, P., Miragaia, R., Fraz\u00e3o, L., Costa, N., Fern\u00e1ndez-Caballero, A., Carneiro, J., Buruberri, L.H., and Pereira, A. (2023). A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection. J. Imaging, 9.","DOI":"10.3390\/jimaging9100193"},{"key":"ref_6","first-page":"22","article-title":"Dynamics-inspired feature extraction in semiconductor manufacturing processes","volume":"13","author":"Haq","year":"2019","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhou, X., Nie, Y., Wang, Y., Cao, P., Ye, M., Tang, Y., and Wang, Z. (2020, January 12\u201313). A Real-time and High-efficiency Surface Defect Detection Method for Metal Sheets Based on Compact CNN. Proceedings of the 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020, Hangzhou, China.","DOI":"10.1109\/ISCID51228.2020.00064"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gai, X., Ye, P., Wang, J., and Wang, B. (2020, January 12\u201314). Research on Defect Detection Method for Steel Metal Surface based on Deep Learning. Proceedings of the 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference, ITOEC 2020, Chongqing, China.","DOI":"10.1109\/ITOEC49072.2020.9141669"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ali, S.B., Wate, R., Kujur, S., Singh, A., and Kumar, S. (2020, January 10\u201313). Wall Crack Detection Using Transfer Learning-based CNN Models. Proceedings of the 2020 IEEE 17th India Council International Conference, INDICO 2020, New Delhi, India.","DOI":"10.1109\/INDICON49873.2020.9342392"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Saeed, M.S. (2021, January 5\u20137). Unmanned Aerial Vehicle for Automatic Detection of Concrete Crack using Deep Learning. Proceedings of the International Conference on Robotics, Electrical and Signal Processing Techniques, Dhaka, Bangladesh.","DOI":"10.1109\/ICREST51555.2021.9331177"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ding, F., Zhuang, Z., Liu, Y., Jiang, D., Yan, X., and Wang, Z. (2020). Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm. Sensors, 20.","DOI":"10.3390\/s20185315"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jung, S.Y., Tsai, Y.H., Chiu, W.Y., Hu, J.S., and Sun, C.T. (2018, January 9\u201312). Defect detection on randomly textured surfaces by convolutional neural networks. Proceedings of the IEEE\/ASME International Conference on Advanced Intelligent Mechatronics, AIM, Auckland, New Zealand.","DOI":"10.1109\/AIM.2018.8452361"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101421","DOI":"10.1016\/j.aei.2021.101421","article-title":"Virtual restoration of the colored paintings on weathered beams in the Forbidden City using multiple deep learning algorithms","volume":"50","author":"Zou","year":"2021","journal-title":"Adv. Eng. Inform."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xu, F., Liu, Y., Zi, B., and Zheng, L. (2021, January 9\u201311). Application of Deep Learning for Defect Detection of Paint Film. Proceedings of the 2021 IEEE 6th International Conference on Intelligent Computing and Signal Processing, ICSP 2021, Xi\u2019an, China.","DOI":"10.1109\/ICSP51882.2021.9408956"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Min, B., Tin, H., Nasridinov, A., and Yoo, K.H. (, January 19\u201322). Abnormal detection and classification in i-ceramic images. Proceedings of the 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020, Busan, Republic of Korea.","DOI":"10.1109\/BigComp48618.2020.0-106"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/0734-189X(85)90016-7","article-title":"Topological structural analysis of digitized binary images by border following","volume":"30","author":"Suzuki","year":"1985","journal-title":"Comput. Vision Graph. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Majeed, F., Shafique, U., Safran, M., Alfarhood, S., and Ashraf, I. (2023). Detection of Drowsiness among Drivers Using Novel Deep Convolutional Neural Network Model. Sensors, 23.","DOI":"10.3390\/s23218741"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_19","unstructured":"Alegre, E., Pajares, G., and De la Escalera, A. (2016). Conceptos y M\u00e9todos en Visi\u00f3n por Computador, Comit\u00e9 Espa\u00f1ol de Autom\u00e1tica (CEA). Chapter 4."},{"key":"ref_20","first-page":"475","article-title":"Comprehensive colour image normalization","volume":"Volume 1406","author":"Finlayson","year":"1998","journal-title":"Proceedings of the ECCV 1998"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s00216-011-4929-z","article-title":"Normalization in MALDI-TOF imaging datasets of proteins: Practical considerations","volume":"401","author":"Deininger","year":"2011","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_22","unstructured":"Loffe, S., and Normalization, C.S.B. (2014). Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"151","DOI":"10.3390\/computers12080151","article-title":"Convolutional Neural Networks: A Survey","volume":"12","author":"Kermanidis","year":"2023","journal-title":"Computers"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Karangwa, J., Kong, L., You, T., and Zheng, J. (2020, January 18\u201320). Automated Surface Defects Detection on Mirrorlike Materials by using Faster R-CNN. Proceedings of the 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020, Changsha, China.","DOI":"10.1109\/ICISCE50968.2020.00341"},{"key":"ref_25","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Advances in Neural Information Processing Systems 25 (NIPS 2012), Proceedings of the 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA, 3\u20136 December 2012, Curran Associates, Incorporated."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abbas, Q., Ahmad, G., Alyas, T., Alghamdi, T., Alsaawy, Y., and Alzahrani, A. (2023). Revolutionizing Urban Mobility: IoT-Enhanced Autonomous Parking Solutions with Transfer Learning for Smart Cities. Sensors, 23.","DOI":"10.3390\/s23218753"},{"key":"ref_27","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s13748-019-00203-0","article-title":"Convolutional neural network: A review of models, methodologies and applications to object detection","volume":"9","author":"Dhillon","year":"2020","journal-title":"Prog. Artif. Intell."},{"key":"ref_29","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105528","DOI":"10.1016\/j.compag.2020.105528","article-title":"Automated sheep facial expression classification using deep transfer learning","volume":"175","author":"Noor","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Boyd, A., Czajka, A., and Bowyer, K. Deep Learning-Based Feature Extraction in Iris Recognition: Use Existing Models, Fine-tune or Train from Scratch? In Proceedings of the 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems, BTAS 2019, Tampa, FL, USA, 23\u201326 September 2019.","DOI":"10.1109\/BTAS46853.2019.9185978"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liang, H., Fu, W., and Yi, F. (2019, January 16\u201319). A Survey of Recent Advances in Transfer Learning. Proceedings of the International Conference on Communication Technology Proceedings, ICCT, Xi\u2019an, China.","DOI":"10.1109\/ICCT46805.2019.8947072"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101387","DOI":"10.1016\/j.csl.2022.101387","article-title":"Train from scratch: Single-stage joint training of speech separation and recognition","volume":"76","author":"Shi","year":"2022","journal-title":"Comput. Speech Lang."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bethge, J., Bornstein, M., Loy, A., Yang, H., and Meinel, C. (2018). Training Competitive Binary Neural Networks from Scratch. arXiv.","DOI":"10.1109\/ICIP.2019.8802610"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mastouri, R., Khlifa, N., Neji, H., and Hantous-Zannad, S. (2020, January 26\u201328). Transfer Learning vs. Fine-Tuning in Bilinear CNN for Lung Nodules Classification on CT Scans. Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition, Xiamen, China.","DOI":"10.1145\/3430199.3430211"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Karungaru, S. (2019, January 20\u201323). Kitchen Utensils Recognition Using Fine Tuning and Transfer Learning. Proceedings of the 3rd International Conference on Video and Image Processing, Shanghai, China.","DOI":"10.1145\/3376067.3376104"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mittel, D., and Kerber, F. (2019, January 10\u201313). Vision-Based Crack Detection using Transfer Learning in Metal Forming Processes. Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Zaragoza, Spain.","DOI":"10.1109\/ETFA.2019.8869084"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1587\/transfun.E102.A.1817","article-title":"Detecting Surface Defects of Wind Tubine Blades Using an Alexnet Deep Learning Algorithm","volume":"102","author":"Zhao","year":"2019","journal-title":"IEICE Trans. Fundam. Electron. Commun. Comput. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1111\/mice.12411","article-title":"Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images","volume":"33","author":"Wang","year":"2018","journal-title":"Comput.-Aided Civ. Infrastruct. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"He, H., Yuan, M., and Liu, X. (2021, January 9\u201311). Research on Surface Defect Detection Method of Metal Workpiece Based on Machine Learning. Proceedings of the 2021 IEEE 6th International Conference on Intelligent Computing and Signal Processing, ICSP 2021, Xi\u2019an, China.","DOI":"10.1109\/ICSP51882.2021.9408778"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Phua, C., and Theng, L.B. (2020, January 16\u201319). Semiconductor wafer surface: Automatic defect classification with deep CNN. Proceedings of the IEEE Region 10 Annual International Conference, Proceedings\/TENCON, Osaka, Japan.","DOI":"10.1109\/TENCON50793.2020.9293715"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4787","DOI":"10.1109\/TIM.2019.2899478","article-title":"Surface Defects Detection Based on Adaptive Multiscale Image Collection and Convolutional Neural Networks","volume":"68","author":"Sun","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/232\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:45:06Z","timestamp":1760132706000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/232"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,31]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24010232"],"URL":"https:\/\/doi.org\/10.3390\/s24010232","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,31]]}}}