{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T08:13:49Z","timestamp":1776586429436,"version":"3.51.2"},"reference-count":22,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T00:00:00Z","timestamp":1747094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Quality control and predictive maintenance are two essential pillars of Industry 4.0, aiming to optimize production, reduce operational costs, and enhance system reliability. Real-time visual inspection ensures early detection of manufacturing defects, assembly errors, or texture inconsistencies, preventing defective products from reaching customers. Predictive maintenance leverages sensor data by analyzing vibrations, temperature, and pressure signals to anticipate failures and avoid production downtime. Image-based quality control has become critical in industries such as automotive, electronics, aerospace, and food processing, where visual appearance is a key quality indicator. Although advances in deep learning and computer vision have significantly improved anomaly detection, industrial deployments remain challenged by the scarcity of labeled anomalies and the variability of defects. These issues increasingly lead to the adoption of unsupervised methods and generative approaches, which, despite their effectiveness, introduce substantial computational complexity. We conduct a unified comparison of ten anomaly detection methods, categorizing them according to their reliance on synthetic anomaly generation and their detection strategy, either reconstruction-based or feature-based. All models are trained exclusively on normal data to mirror realistic industrial conditions. Our evaluation framework combines performance metrics such as recall, precision, and their harmonic mean, emphasizing the need to minimize false negatives that could lead to critical production failures. In addition, we assess environmental impact and hardware complexity to better guide method selection. Practical recommendations are provided to balance robustness, operational feasibility, and sustainability in industrial applications.<\/jats:p>","DOI":"10.3390\/bdcc9050128","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T03:59:26Z","timestamp":1747108766000},"page":"128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Benchmarking of Anomaly Detection Methods for Industry 4.0: Evaluation, Ranking, and Practical Recommendations"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2656-351X","authenticated-orcid":false,"given":"Aur\u00e9lie","family":"Cools","sequence":"first","affiliation":[{"name":"Department of Computer Science, Software and Artificial Intelligence, Faculty of Engineering (Polytechnic Faculty), University of Mons (UMons), 7000 Mons, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1169-5744","authenticated-orcid":false,"given":"Mohammed Amin","family":"Belarbi","sequence":"additional","affiliation":[{"name":"Amintechs, 7000 Mons, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1530-9524","authenticated-orcid":false,"given":"Sidi Ahmed","family":"Mahmoudi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Software and Artificial Intelligence, Faculty of Engineering (Polytechnic Faculty), University of Mons (UMons), 7000 Mons, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,13]]},"reference":[{"key":"ref_1","first-page":"18","article-title":"A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems","volume":"3","author":"Lee","year":"2015","journal-title":"Manuf. Lett."},{"key":"ref_2","first-page":"25","article-title":"Smart manufacturing systems for Industry 4.0: Conceptual framework, architecture and key technologies","volume":"48","author":"Lu","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/JPROC.2021.3052449","article-title":"A unifying review of deep and shallow anomaly detection","volume":"109","author":"Ruff","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C., and Shah, M. (2018, January 18\u201323). Real-world anomaly detection in surveillance videos. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00678"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., and Steger, C. (2019, January 15\u201320). MVTEC AD: A comprehensive real-world dataset for unsupervised anomaly detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, Q., Luo, H., Lv, C., and Zhang, Z. (2024). A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization. Computer Vision\u2014ECCV 2024, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-031-72855-6_3"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2016). Wide Residual Networks. arXiv.","DOI":"10.5244\/C.30.87"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, C.-L., Sohn, K., Yoon, J., and Pfister, T. (2021, January 20\u201325). CutPaste: Self-Supervised Learning for Anomaly Detection and Localization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Roth, K., Pemula, L., Zepeda, J., Sch\u00f6lkopf, B., Brox, T., and Gehler, P. (2022, January 18\u201324). Towards Total Recall in Industrial Anomaly Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA. Available online: https:\/\/openaccess.thecvf.com\/content\/CVPR2022\/papers\/Roth_Towards_Total_Recall_in_Industrial_Anomaly_Detection_CVPR_2022_paper.pdf.","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"ref_10","unstructured":"Yi, J., and Yoon, S. (December, January 30). Patch SVDD: Patch-Level SVDD for Anomaly Detection and Segmentation. Proceedings of the Asian Conference on Computer Vision (ACCV), Kyoto, Japan."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tsai, C.C., Wu, T.H., and Lai, S.H. (2022, January 3\u20138). Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA. Available online: https:\/\/openaccess.thecvf.com\/content\/WACV2022\/papers\/Tsai_Multi-Scale_Patch-Based_Representation_Learning_for_Image_Anomaly_Detection_and_Segmentation_WACV_2022_paper.pdf.","DOI":"10.1109\/WACV51458.2022.00312"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M., and Sko\u010daj, D. (2021, January 10\u201317). DRAEM\u2014A Discriminatively Trained Reconstruction Embedding for Surface Anomaly Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada. Available online: https:\/\/openaccess.thecvf.com\/content\/ICCV2021\/papers\/Zavrtanik_DRAEM_-_A_Discriminatively_Trained_Reconstruction_Embedding_for_Surface_Anomaly_ICCV_2021_paper.pdf.","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"ref_13","unstructured":"Zhang, H., Wang, Z., Wu, Z., and Jiang, Y.G. (2023). DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107706","DOI":"10.1016\/j.patcog.2020.107706","article-title":"Reconstruction by Inpainting for Visual Anomaly Detection","volume":"112","author":"Zavrtanik","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany, 5\u20139 October 2015, Proceedings, Part III, Springer Nature.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cremers, D., L\u00e4hner, Z., Moeller, M., Nie\u00dfner, M., Ommer, B., and Triebel, R. (2025). Anomaly Detection with Conditioned Denoising Diffusion Models. Pattern Recognition. DAGM GCPR 2024, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-031-85181-0"},{"key":"ref_17","unstructured":"Guo, J., Lu, S., Zhang, W., Chen, F., Li, H., and Liao, H. (2025, January 11\u201315). Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA."},{"key":"ref_18","unstructured":"Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., and El-Nouby, A. (2025, February 15). DINOv2: Learning Robust Visual Features Without Supervision. Transactions on Machine Learning Research. Available online: https:\/\/openreview.net\/forum?id=a68SUt6zFt."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Davis, J., and Goadrich, M. (2006, January 25\u201329). The relationship between precision-recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143874"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Saito, T., and Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118432"},{"key":"ref_21","unstructured":"Van Rijsbergen, C.J. (1979). Information Retrieval, Butterworth-Heinemann."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/5\/128\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:31:34Z","timestamp":1760031094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/5\/128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,13]]},"references-count":22,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["bdcc9050128"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9050128","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,13]]}}}