{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:50:15Z","timestamp":1778604615855,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T00:00:00Z","timestamp":1737072000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PRR\u2014Plano de Recupera\u00e7\u00e3o e Resili\u00eancia under the Next Generation EU from the European Union","award":["C644919832-00000035"],"award-info":[{"award-number":["C644919832-00000035"]}]},{"name":"PRR\u2014Plano de Recupera\u00e7\u00e3o e Resili\u00eancia under the Next Generation EU from the European Union","award":["UIDB\/00481\/2020"],"award-info":[{"award-number":["UIDB\/00481\/2020"]}]},{"name":"PRR\u2014Plano de Recupera\u00e7\u00e3o e Resili\u00eancia under the Next Generation EU from the European Union","award":["UIDP\/00481\/2020"],"award-info":[{"award-number":["UIDP\/00481\/2020"]}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia I.P. 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(FCT, IP)","doi-asserted-by":"publisher","award":["UIDP\/00481\/2020"],"award-info":[{"award-number":["UIDP\/00481\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To automate the quality control of painted surfaces of heating devices, an automatic defect detection and classification system was developed by combining deflectometry and bright light-based illumination on the image acquisition, deep learning models for the classification of non-defective (OK) and defective (NOK) surfaces that fused dual-modal information at the decision level, and an online network for information dispatching and visualization. Three decision-making algorithms were tested for implementation: a new model built and trained from scratch and transfer learning of pre-trained networks (ResNet-50 and Inception V3). The results revealed that the two illumination modes employed widened the type of defects that could be identified with this system, while maintaining its lower computational complexity by performing multi-modal fusion at the decision level. Furthermore, the pre-trained networks achieved higher accuracies on defect classification compared to the self-built network, with ResNet-50 displaying higher accuracy. The inspection system consistently obtained fast and accurate surface classifications because it imposed OK classification on models trained with images from both illumination modes. The obtained surface information was then successfully sent to a server to be forwarded to a graphical user interface for visualization. The developed system showed considerable robustness, demonstrating its potential as an efficient tool for industrial quality control.<\/jats:p>","DOI":"10.3390\/s25020527","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T11:24:56Z","timestamp":1737113096000},"page":"527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Improving Industrial Quality Control: A Transfer Learning Approach to Surface Defect Detection"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4149-3804","authenticated-orcid":false,"given":"\u00c2ngela","family":"Semitela","sequence":"first","affiliation":[{"name":"Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Intelligent Systems Associate Laboratory (LASI), 4800-058 Guimar\u00e3es, Portugal"}]},{"given":"Miguel","family":"Pereira","sequence":"additional","affiliation":[{"name":"Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3972-8432","authenticated-orcid":false,"given":"Ant\u00f3nio","family":"Completo","sequence":"additional","affiliation":[{"name":"Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Intelligent Systems Associate Laboratory (LASI), 4800-058 Guimar\u00e3es, Portugal"}]},{"given":"Nuno","family":"Lau","sequence":"additional","affiliation":[{"name":"Intelligent Systems Associate Laboratory (LASI), 4800-058 Guimar\u00e3es, Portugal"},{"name":"Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Department of Electronics, Telecommunications and Informatics, University of Aveiro, 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0417-8167","authenticated-orcid":false,"given":"Jos\u00e9 P.","family":"Santos","sequence":"additional","affiliation":[{"name":"Centre of Mechanical Technology and Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal"},{"name":"Intelligent Systems Associate Laboratory (LASI), 4800-058 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1080\/0951192X.2021.1891573","article-title":"Intelligent Machining: A Review of Trends, Achievements and Current Progress","volume":"35","author":"Imad","year":"2022","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.rcim.2017.04.009","article-title":"On the Detection of Defects on Specular Car Body Surfaces","volume":"48","author":"Molina","year":"2017","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Armesto, L., Tornero, J., Herraez, A., and Asensio, J. (2011, January 9\u201313). Inspection System Based on Artificial Vision for Paint Defects Detection on Cars Bodies. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980570"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"171","DOI":"10.25046\/aj040323","article-title":"An Efficient Automotive Paint Defect Detection System","volume":"4","author":"Akhtar","year":"2019","journal-title":"Adv. Sci. Technol. Eng. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1080\/0951192X.2020.1795928","article-title":"Leather Defect Classification and Segmentation Using Deep Learning Architecture","volume":"33","author":"Liong","year":"2020","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5298","DOI":"10.1109\/TIM.2019.2962565","article-title":"A Lightweight Appearance Quality Assessment System Based on Parallel Deep Learning for Painted Car Body","volume":"69","author":"Chang","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107488","DOI":"10.1016\/j.optlaseng.2023.107488","article-title":"Defect Classification for Specular Surfaces Based on Deflectometry and Multi-Modal Fusion Network","volume":"163","author":"Guan","year":"2023","journal-title":"Opt. Lasers Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.measurement.2005.12.007","article-title":"Inspection of Specular and Painted Surfaces with Centralized Fusion Techniques","volume":"39","author":"Kammel","year":"2006","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104042","DOI":"10.1016\/j.imavis.2020.104042","article-title":"Deep Multimodal Fusion for Semantic Image Segmentation: A Survey","volume":"105","author":"Zhang","year":"2021","journal-title":"Image Vis. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Berwo, M.A., Fang, Y., Mahmood, J., Yang, N., Liu, Z., and Li, Y. (2022). FAECCD-CNet: Fast Automotive Engine Components Crack Detection and Classification Using ConvNet on Images. Appl. Sci., 12.","DOI":"10.3390\/app12199713"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.jmapro.2020.12.067","article-title":"A Vision-Based Method for Lap Weld Defects Monitoring of Galvanized Steel Sheets Using Convolutional Neural Network","volume":"64","author":"Ma","year":"2021","journal-title":"J. Manuf. Process"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"119623","DOI":"10.1016\/j.eswa.2023.119623","article-title":"Comparative Assessment of Common Pre-Trained CNNs for Vision-Based Surface Defect Detection of Machined Components","volume":"218","author":"Singh","year":"2023","journal-title":"Expert. Syst. Appl."},{"key":"ref_13","first-page":"148","article-title":"A Combined Approach of Convolutional Neural Networks and Machine Learning for Visual Fault Classification in Photovoltaic Modules","volume":"236","author":"Venkatesh","year":"2022","journal-title":"Proc. Inst. Mech. Eng. O. J. Risk Reliab."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Trofimov, A.G., and Bogatyreva, A.A. (2019, January 7\u201311). A Method of Choosing a Pre-Trained Convolutional Neural Network for Transfer Learning in Image Classification Problems. Proceedings of the Advances in Neural Computation, Machine Learning, and Cognitive Research III: Neuroinformatics 2019, Dolgoprudny, Moscow Region, Russia.","DOI":"10.1007\/978-3-030-30425-6_31"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Semitela, \u00c2., Ferreira, A., Completo, A., Lau, N., and Santos, J.P. (2024). Detecting and Classifying Defects on the Surface of Water Heaters: Development of an Automated System. Proc. Inst. Mech. Eng. Part. E J. Process Mech. Eng.","DOI":"10.1177\/09544089241262945"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/S0262-8856(02)00152-X","article-title":"A Survey on Industrial Vision Systems, Applications and Tools","volume":"21","author":"Malamas","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s40684-021-00343-6","article-title":"State of the Art in Defect Detection Based on Machine Vision","volume":"9","author":"Ren","year":"2022","journal-title":"Int. J. Precis. Eng. Manuf. Green Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.cirp.2022.05.004","article-title":"The Implication and Evaluation of Geometrical Imperfections on Manufactured Surfaces","volume":"71","author":"Mullany","year":"2022","journal-title":"CIRP Ann."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, Z., Yao, Y., Wen, R., and Liu, Q. (2024). Dual-Modal Illumination System for Defect Detection of Aircraft Glass Canopies. Sensors, 24.","DOI":"10.3390\/s24206717"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Peng, X., Kong, L., Han, W., and Wang, S. (2022). Multi-Sensor Image Fusion Method for Defect Detection in Powder Bed Fusion. Sensors, 22.","DOI":"10.3390\/s22208023"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s13634-023-01002-5","article-title":"Decision-Level Fusion Detection Method of Visible and Infrared Images under Low Light Conditions","volume":"2023","author":"Hu","year":"2023","journal-title":"EURASIP J. Adv. Signal Process"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"626001","DOI":"10.3788\/IRLA201948.0626001","article-title":"Decision-Level Fusion Detection for Infrared and Visible Spectra Based on Deep Learning","volume":"48","author":"Cong","year":"2019","journal-title":"Infrared Laser Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107834","DOI":"10.1016\/j.measurement.2020.107834","article-title":"DeepInspection: Deep Learning Based Hierarchical Network for Specular Surface Inspection","volume":"160","author":"Zhou","year":"2020","journal-title":"Measurement"},{"key":"ref_25","first-page":"40","article-title":"Deep Convolutional Neural Networks: Structure, Feature Extraction and Training","volume":"20","year":"2018","journal-title":"Inf. Technol. Manag. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Taye, M.M. (2023). Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions. Computation, 11.","DOI":"10.3390\/computation11030052"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"739","DOI":"10.31763\/ijrcs.v2i4.888","article-title":"ul Understanding of Convolutional Neural Network (CNN): A Review","volume":"2","author":"Purwono","year":"2023","journal-title":"Int. J. Robot. Control. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Luo, C., Li, X., Wang, L., He, J., Li, D., and Zhou, J. (2018, January 10\u201312). How Does the Data Set Affect CNN-Based Image Classification Performance?. Proceedings of the 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China.","DOI":"10.1109\/ICSAI.2018.8599448"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.neucom.2019.09.107","article-title":"A Learning-Based Approach for Surface Defect Detection Using Small Image Datasets","volume":"408","author":"Le","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_30","unstructured":"Ghahramani, Z., Weinberger, K.Q., Cortes, C., Lawrence, N.D., and Welling, M. (2014). How Transferable Are Features in Deep Neural Networks?. Proceedings of the 28th Annual Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation."},{"key":"ref_31","first-page":"103569","article-title":"Ten Deep Learning Techniques to Address Small Data Problems with Remote Sensing","volume":"125","author":"Safonova","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A Survey of Transfer Learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.jare.2021.03.015","article-title":"A Review on Modern Defect Detection Models Using DCNNs\u2014Deep Convolutional Neural Networks","volume":"35","author":"Tulbure","year":"2022","journal-title":"J. Adv. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1007\/s10462-022-10213-5","article-title":"A Review of Convolutional Neural Network Architectures and Their Optimizations","volume":"56","author":"Cong","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of Deep Learning: Concepts, CNN Architectures, Challenges, Applications, Future Directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, L., Bian, Y., Jiang, P., and Zhang, F. (2023). A Transfer Residual Neural Network Based on ResNet-50 for Detection of Steel Surface Defects. Appl. Sci., 13.","DOI":"10.3390\/app13095260"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1007\/s00170-024-13768-5","article-title":"Smart Defect Detection Using Transfer Learning in Injection Molding: A Comparative Exploration Study of Deep Learning Architectures","volume":"133","author":"Mouchtachi","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_38","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Park, H., Kang, Y.S., Choi, S.-K., and Park, H.W. (2024). Quality Evaluation Modeling of a DED-Processed Metallic Deposition Based on ResNet-50 with Few Training Data. J. Intell. Manuf.","DOI":"10.1007\/s10845-024-02408-0"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kiran, G.U., Gajula, S., Sravanthi, A., Srilakshmi, V., Veditha, T., and Reddy, D.V. (2024, January 4\u20136). Inception V3 Model-Based Approach for Detecting Defects on Steel Surfaces. Proceedings of the 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India.","DOI":"10.1109\/IDCIoT59759.2024.10467368"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Merino, I., Azpiazu, J., Remazeilles, A., and Sierra, B. (2021). 3D Convolutional Neural Networks Initialized from Pretrained 2D Convolutional Neural Networks for Classification of Industrial Parts. Sensors, 21.","DOI":"10.3390\/s21041078"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Qayyum, W., Ehtisham, R., Bahrami, A., Camp, C., Mir, J., and Ahmad, A. (2023). Assessment of Convolutional Neural Network Pre-Trained Models for Detection and Orientation of Cracks. Materials, 16.","DOI":"10.3390\/ma16020826"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.gltp.2021.08.027","article-title":"ResNet-50 vs VGG-19 vs Training from Scratch: A Comparative Analysis of the Segmentation and Classification of Pneumonia from Chest X-Ray Images","volume":"2","author":"Murali","year":"2021","journal-title":"Glob. Transit. Proc."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mao, W.-L., Chiu, Y.-Y., Lin, B.-H., Wang, C.-C., Wu, Y.-T., You, C.-Y., and Chien, Y.-R. (2022). Integration of Deep Learning Network and Robot Arm System for Rim Defect Inspection Application. Sensors, 22.","DOI":"10.3390\/s22103927"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s44196-024-00423-w","article-title":"Deep Learning-Based Integrated Circuit Surface Defect Detection: Addressing Information Density Imbalance for Industrial Application","volume":"17","author":"Wang","year":"2024","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1007\/s10845-021-01878-w","article-title":"Automated Surface Defect Detection Framework Using Machine Vision and Convolutional Neural Networks","volume":"34","author":"Singh","year":"2022","journal-title":"J. Intell. Manuf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"123276","DOI":"10.1016\/j.eswa.2024.123276","article-title":"ResNet Deep Models and Transfer Learning Technique for Classification and Quality Detection of Rice Cultivars","volume":"247","author":"Razavi","year":"2024","journal-title":"Expert. Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.polymertesting.2013.12.008","article-title":"Detection of Visually Perceptible Sink Marks on High Gloss Injection Molded Parts by Phase Measuring Deflectometry","volume":"34","author":"Macher","year":"2014","journal-title":"Polym. Test."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.jmapro.2021.08.034","article-title":"Defect Detection of Injection Molding Products on Small Datasets Using Transfer Learning","volume":"70","author":"Liu","year":"2021","journal-title":"J. Manuf. 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