{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:32:47Z","timestamp":1779294767045,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Horizon 2020","award":["814225"],"award-info":[{"award-number":["814225"]}]},{"name":"Horizon 2020","award":["CER20211007"],"award-info":[{"award-number":["CER20211007"]}]},{"name":"5R- Red Cervera de Tecnolog\u00edas rob\u00f3ticas en fabricaci\u00f3n inteligente","award":["814225"],"award-info":[{"award-number":["814225"]}]},{"name":"5R- Red Cervera de Tecnolog\u00edas rob\u00f3ticas en fabricaci\u00f3n inteligente","award":["CER20211007"],"award-info":[{"award-number":["CER20211007"]}]},{"name":"The Centre for the Development of Industrial Technology (CDTI)","award":["814225"],"award-info":[{"award-number":["814225"]}]},{"name":"The Centre for the Development of Industrial Technology (CDTI)","award":["CER20211007"],"award-info":[{"award-number":["CER20211007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface defect identification based on computer vision algorithms often leads to inadequate generalization ability due to large intraclass variation. Diversity in lighting conditions, noise components, defect size, shape, and position make the problem challenging. To solve the problem, this paper develops a pixel-level image augmentation method that is based on image-to-image translation with generative adversarial neural networks (GANs) conditioned on fine-grained labels. The GAN model proposed in this work, referred to as Magna-Defect-GAN, is capable of taking control of the image generation process and producing image samples that are highly realistic in terms of variations. Firstly, the surface defect dataset based on the magnetic particle inspection (MPI) method is acquired in a controlled environment. Then, the Magna-Defect-GAN model is trained, and new synthetic image samples with large intraclass variations are generated. These synthetic image samples artificially inflate the training dataset size in terms of intraclass diversity. Finally, the enlarged dataset is used to train a defect identification model. Experimental results demonstrate that the Magna-Defect-GAN model can generate realistic and high-resolution surface defect images up to the resolution of 512 \u00d7 512 in a controlled manner. We also show that this augmentation method can boost accuracy and be easily adapted to any other surface defect identification models.<\/jats:p>","DOI":"10.3390\/s23041861","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T02:04:16Z","timestamp":1675821856000},"page":"1861","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Intraclass Image Augmentation for Defect Detection Using Generative Adversarial Neural Networks"],"prefix":"10.3390","volume":"23","author":[{"given":"Vignesh","family":"Sampath","sequence":"first","affiliation":[{"name":"Smart and Autonomous System Unit, Tekniker, Member of Basque Research & Technology Alliance, 20600 Eibar, Spain"},{"name":"Department of Design and Manufacturing Engineering, School of Engineering and Architecture, University of Zaragoza, 50009 Zaragoza, Spain"}]},{"given":"I\u00f1aki","family":"Maurtua","sequence":"additional","affiliation":[{"name":"Smart and Autonomous System Unit, Tekniker, Member of Basque Research & Technology Alliance, 20600 Eibar, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8609-1358","authenticated-orcid":false,"given":"Juan Jos\u00e9","family":"Aguilar Mart\u00edn","sequence":"additional","affiliation":[{"name":"Department of Design and Manufacturing Engineering, School of Engineering and Architecture, University of Zaragoza, 50009 Zaragoza, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2760-435X","authenticated-orcid":false,"given":"Ander","family":"Iriondo","sequence":"additional","affiliation":[{"name":"Smart and Autonomous System Unit, Tekniker, Member of Basque Research & Technology Alliance, 20600 Eibar, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9192-3879","authenticated-orcid":false,"given":"Iker","family":"Lluvia","sequence":"additional","affiliation":[{"name":"Smart and Autonomous System Unit, Tekniker, Member of Basque Research & Technology Alliance, 20600 Eibar, Spain"}]},{"given":"Gotzone","family":"Aizpurua","sequence":"additional","affiliation":[{"name":"Smart and Autonomous System Unit, Tekniker, Member of Basque Research & Technology Alliance, 20600 Eibar, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3690","DOI":"10.1016\/j.matpr.2017.11.620","article-title":"Advances and Researches on Non Destructive Testing: A Review","volume":"5","author":"Dwivedi","year":"2018","journal-title":"Mater. Today Proc."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sampath, V., Maurtua, I., Martin, J.J.A., Iriondo, A., Lluvia, I., and Rivera, A. (2022, January 20\u201322). Vision Transformer based knowledge distillation for fasteners defect detection. Proceedings of the 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), Prague, Czech Republic.","DOI":"10.1109\/ICECET55527.2022.9872566"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sampath, V., Maurtua, I., Martin, J.J.A., Rivera, A., Molina, J., and Gutierrez, A. (2023). Attention Guided Multi-Task Learning for Surface defect identification. IEEE Trans. Ind. Inform., early access.","DOI":"10.1109\/TII.2023.3234030"},{"key":"ref_4","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional Generative Adversarial Nets. arXiv."},{"key":"ref_5","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_6","first-page":"13001","article-title":"Random Erasing Data Augmentation","volume":"34","author":"Zhong","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"FMoreno-Barea, J., Strazzera, F., Jerez, J.M., Urda, D., and Franco, L. (2018, January 18\u201321). Forward Noise Adjustment Scheme for Data Augmentation. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628917"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"ECubuk, D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q.V. (2018). AutoAugment: Learning Augmentation Policies from Data. arXiv.","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref_9","unstructured":"Perez, L., and Wang, J. (2017). The Effectiveness of Data Augmentation in Image Classification using Deep Learning. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40537-021-00414-0","article-title":"A survey on generative adversarial networks for imbalance problems in computer vision tasks","volume":"8","author":"Sampath","year":"2021","journal-title":"J. Big Data"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_12","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv."},{"key":"ref_13","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive Growing of GANs for Improved Quality, Stability, and Variation. arXiv."},{"key":"ref_14","unstructured":"Denton, E., Chintala, S., Szlam, A., and Fergus, R. (2015). Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. arXiv."},{"key":"ref_15","unstructured":"Im, D.J., Kim, C.D., Jiang, H., and Memisevic, R. (2016). Generating images with recurrent adversarial networks. arXiv."},{"key":"ref_16","unstructured":"Nguyen, T.D., Le, T., Vu, H., and Phung, D. (2017). Dual Discriminator Generative Adversarial Nets. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1007\/s00170-022-09356-0","article-title":"Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models","volume":"121","author":"Shah","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Kulharia, V., Namboodiri, V., Torr, P.H.S., and Dokania, P.K. (2017). Multi-Agent Diverse Generative Adversarial Networks. arXiv.","DOI":"10.1109\/CVPR.2018.00888"},{"key":"ref_19","unstructured":"Odena, A., Olah, C., and Shlens, J. (2016). Conditional Image Synthesis with Auxiliary Classifier GANs. arXiv."},{"key":"ref_20","unstructured":"Bazrafkan, S., and Corcoran, P. (2018). Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN), Multi Class Scenarios. arXiv."},{"key":"ref_21","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P. (2016). InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"147928","DOI":"10.1109\/ACCESS.2018.2872695","article-title":"SCGAN: Disentangled Representation Learning by Adding Similarity Constraint on Generative Adversarial Nets","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016). Improved Techniques for Training GANs. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101632","DOI":"10.1016\/j.bspc.2019.101632","article-title":"DN-GAN: Denoising generative adversarial networks for speckle noise reduction in optical coherence tomography images","volume":"55","author":"Chen","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xu, Y., Li, H., Zhang, S., Wang, X., Huang, X., and Metaxas, D. (2016). StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. arXiv.","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A.A. (2016). Image-to-Image Translation with Conditional Adversarial Networks. arXiv.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4066","DOI":"10.1109\/TIP.2018.2836316","article-title":"Perceptual Adversarial Networks for Image-to-Image Transformation","volume":"27","author":"Wang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","unstructured":"Kim, T., Cha, M., Kim, H., Lee, J.K., and Kim, J. (2017). Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., and Efros, A.A. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. arXiv.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6743","DOI":"10.1109\/TII.2021.3126098","article-title":"Mask2Defect: A Prior Knowledge-Based Data Augmentation Method for Metal Surface Defect Inspection","volume":"18","author":"Yang","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_31","first-page":"1611","article-title":"Defect Image Sample Generation With GAN for Improving Defect Recognition","volume":"17","author":"Niu","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, G., Cui, K., Hung, T.-Y., and Lu, S. (2021, January 5\u20139). Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Virtual.","DOI":"10.1109\/WACV48630.2021.00257"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"118788","DOI":"10.1016\/j.eswa.2022.118788","article-title":"Multi-scale GAN with transformer for surface defect inspection of IC metal packages","volume":"212","author":"Chen","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4531","DOI":"10.1109\/TII.2021.3127188","article-title":"Region- and Strength-Controllable GAN for Defect Generation and Segmentation in Industrial Images","volume":"18","author":"Niu","year":"2022","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, X., and Gupta, A. (2016). Generative Image Modeling using Style and Structure Adversarial Networks. arXiv.","DOI":"10.1007\/978-3-319-46493-0_20"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2018). A Style-Based Generator Architecture for Generative Adversarial Networks. arXiv.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA. Available online: https:\/\/proceedings.mlr.press\/v97\/tan19a.html."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1861\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:26:59Z","timestamp":1760120819000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/4\/1861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,7]]},"references-count":38,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23041861"],"URL":"https:\/\/doi.org\/10.3390\/s23041861","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,7]]}}}