{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T07:03:50Z","timestamp":1784531030977,"version":"3.55.0"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100009532","name":"Ministry of the Interior of the Czech Republic","doi-asserted-by":"publisher","award":["VK01010153"],"award-info":[{"award-number":["VK01010153"]}],"id":[{"id":"10.13039\/100009532","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Image and Vision Computing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.imavis.2026.106077","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T15:28:14Z","timestamp":1781537294000},"page":"106077","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["EMAN: Efficient feature modulation and aggregation network for defect classification in industrial images"],"prefix":"10.1016","volume":"173","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4609-3369","authenticated-orcid":false,"given":"Vojtech","family":"Schiller","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8965-6193","authenticated-orcid":false,"given":"Anzhelika","family":"Mezina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1849-5390","authenticated-orcid":false,"given":"Radim","family":"Burget","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2462-737X","authenticated-orcid":false,"given":"Malay Kishore","family":"Dutta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.imavis.2026.106077_b1","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/978-3-030-63986-0_8","article-title":"Industry 4.0 in welding","author":"Mishra","year":"2021","journal-title":"Weld. Technol."},{"issue":"4","key":"10.1016\/j.imavis.2026.106077_b2","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s10921-015-0315-7","article-title":"GDXray: The database of X-ray images for nondestructive testing","volume":"34","author":"Mery","year":"2015","journal-title":"J. Nondestruct. Eval."},{"key":"10.1016\/j.imavis.2026.106077_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpbup.2024.100146","article-title":"AI in diagnostic imaging: Revolutionising accuracy and efficiency","author":"Khalifa","year":"2024","journal-title":"Comput. Methods Programs Biomed. Update"},{"issue":"9","key":"10.1016\/j.imavis.2026.106077_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e30590","article-title":"Deep convolutional neural network for weld defect classification in radiographic images","volume":"10","author":"Palma-Ram\u00edrez","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.imavis.2026.106077_b5","series-title":"AIP Conference Proceedings","doi-asserted-by":"crossref","DOI":"10.1063\/1.5048766","article-title":"X-ray weld image classification using improved convolutional neural network","volume":"Vol. 1995","author":"Yang","year":"2018"},{"issue":"7","key":"10.1016\/j.imavis.2026.106077_b6","doi-asserted-by":"crossref","first-page":"7430","DOI":"10.1109\/JSEN.2023.3247006","article-title":"LF-YOLO: A lighter and faster yolo for weld defect detection of X-ray image","volume":"23","author":"Liu","year":"2023","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.imavis.2026.106077_b7","doi-asserted-by":"crossref","unstructured":"R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization, in: Proceedings of the IEEE International Conference on Computer Vision, ICCV, 2017.","DOI":"10.1109\/ICCV.2017.74"},{"issue":"15","key":"10.1016\/j.imavis.2026.106077_b8","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1080\/08839514.2021.1975391","article-title":"Deep learning based steel pipe weld defect detection","volume":"35","author":"Yang","year":"2021","journal-title":"Appl. Artif. Intell."},{"key":"10.1016\/j.imavis.2026.106077_b9","series-title":"2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery","first-page":"1051","article-title":"WDXI: The dataset of X-ray image for weld defects","author":"Guo","year":"2018"},{"issue":"1","key":"10.1016\/j.imavis.2026.106077_b10","doi-asserted-by":"crossref","first-page":"13","DOI":"10.53375\/ijecer.2023.320","article-title":"RIAWELC: a Novel dataset of radiographic images for automatic weld defects classification","volume":"3","author":"Totino","year":"2023","journal-title":"Int. J. Electr. Comput. Eng. Res."},{"issue":"1","key":"10.1016\/j.imavis.2026.106077_b11","first-page":"1","article-title":"LoHi-WELD: a novel industrial dataset for weld defect detection and classification, a deep learning study, and future perspectives","volume":"1","author":"BLock","year":"2024","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.imavis.2026.106077_b12","doi-asserted-by":"crossref","first-page":"1562","DOI":"10.3390\/s20061562","article-title":"Deep metallic surface defect detection: The new benchmark and detection network","volume":"20","author":"Lv","year":"2020","journal-title":"Sensors"},{"key":"10.1016\/j.imavis.2026.106077_b13","unstructured":"M.-M. Naddaf-Sh, S. Naddaf-Sh, H. Zargaradeh, S.M. Zahiri, M. Dalton, G. Elpers, A.R. Kashani, Next-generation of weld quality assessment using deep learning and digital radiography, in: Artificial Intelligence in Manufacturing, AAAI Spring Symposium Series, 2020."},{"key":"10.1016\/j.imavis.2026.106077_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108379","article-title":"Synthetic data augmentation for high-resolution X-ray welding defect detection and classification based on a small number of real samples","volume":"133","author":"Li","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.imavis.2026.106077_b15","first-page":"29","article-title":"Welding defects classification through a Convolutional Neural Network","volume":"35","author":"Perri","year":"2023","journal-title":"Manuf. Lett."},{"issue":"11","key":"10.1016\/j.imavis.2026.106077_b16","doi-asserted-by":"crossref","first-page":"2963","DOI":"10.1007\/s40194-024-01759-9","article-title":"WeldNet: a lightweight deep learning model for welding defect recognition","volume":"68","author":"Wang","year":"2024","journal-title":"Weld. World"},{"key":"10.1016\/j.imavis.2026.106077_b17","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.jmapro.2019.07.020","article-title":"Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks","volume":"45","author":"Bacioiu","year":"2019","journal-title":"J. Manuf. Process."},{"issue":"6","key":"10.1016\/j.imavis.2026.106077_b18","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1007\/s40194-022-01281-w","article-title":"X-ray weld defect detection based on AF-RCNN","volume":"66","author":"Liu","year":"2022","journal-title":"Weld. World"},{"key":"10.1016\/j.imavis.2026.106077_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108961","article-title":"Ensemble-based deep learning model for welding defect detection and classification","volume":"136","author":"Vasan","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"23","key":"10.1016\/j.imavis.2026.106077_b20","doi-asserted-by":"crossref","first-page":"11893","DOI":"10.3390\/app122311893","article-title":"Defect detection for wear debris based on few-shot contrastive learning","volume":"12","author":"Li","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.imavis.2026.106077_b21","article-title":"EfficientNet-ECA: A lightweight network based on efficient channel attention for class-imbalanced welding defects classification","volume":"62","author":"Zhang","year":"2024","journal-title":"Adv. Eng. Inform."},{"key":"10.1016\/j.imavis.2026.106077_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.ndteint.2019.102139","article-title":"Automated defect classification of SS304 TIG welding process using visible spectrum camera and machine learning","volume":"107","author":"Bacioiu","year":"2019","journal-title":"NDT & E Int."},{"key":"10.1016\/j.imavis.2026.106077_b23","series-title":"Proceedings of International Conference on Artificial Life and Robotics","first-page":"877","article-title":"Deep learning in manufacturing: A focus on welding defect classification with CNNs","volume":"Vol. 29","author":"Ting","year":"2024"},{"key":"10.1016\/j.imavis.2026.106077_b24","first-page":"281","article-title":"Research on the recognition of welding defects by modified ViT model combined with Canny algorithm","volume":"2024","author":"Wang","year":"2025","journal-title":"IET Conf. Proc."},{"issue":"9","key":"10.1016\/j.imavis.2026.106077_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e30590","article-title":"Deep convolutional neural network for weld defect classification in radiographic images","volume":"10","author":"Palma-Ram\u00edrez","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.imavis.2026.106077_b26","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ae09ce","article-title":"Meta-learning enhanced classification of complex defects in pressure vessels","volume":"36","author":"Xia","year":"2025","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.imavis.2026.106077_b27","first-page":"1","article-title":"Welding defects recognition based on DCP-MobileViT network","author":"Zhang","year":"2024","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.imavis.2026.106077_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108961","article-title":"Ensemble-based deep learning model for welding defect detection and classification","volume":"136","author":"Vasan","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.imavis.2026.106077_b29","doi-asserted-by":"crossref","first-page":"13","DOI":"10.53375\/ijecer.2023.320","article-title":"RIAWELC: A novel dataset of radiographic images for automatic weld defects classification","volume":"3","author":"Totino","year":"2023","journal-title":"Int. J. Electr. Comput. Eng. Res."},{"key":"10.1016\/j.imavis.2026.106077_b30","article-title":"Weld-CNN: Advancing non-destructive testing with a hybrid deep learning model for weld defect detection","volume":"17","author":"Hoa","year":"2025","journal-title":"Adv. Mech. Eng."},{"key":"10.1016\/j.imavis.2026.106077_b31","series-title":"MobileNetV4 \u2013 Universal models for the mobile ecosystem","author":"Qin","year":"2024"},{"key":"10.1016\/j.imavis.2026.106077_b32","series-title":"ECA-Net: Efficient channel attention for deep convolutional neural networks","author":"Wang","year":"2019"},{"key":"10.1016\/j.imavis.2026.106077_b33","unstructured":"D. Kingma, J. Ba, Adam: A Method for Stochastic Optimization, in: International Conference on Learning Representations, 2014."},{"issue":"4","key":"10.1016\/j.imavis.2026.106077_b34","first-page":"140","article-title":"StitchingNet and deep transfer learning method for sewing stitch defect detection","volume":"12","author":"Jung","year":"2025","journal-title":"J. Comput. Des. Eng."},{"key":"10.1016\/j.imavis.2026.106077_b35","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1016\/j.apsusc.2013.09.002","article-title":"A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects","volume":"285","author":"Song","year":"2013","journal-title":"Appl. Surf. Sci."},{"key":"10.1016\/j.imavis.2026.106077_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2021.107643","article-title":"Industrial machine tool component surface defect dataset","volume":"39","author":"Schlagenhauf","year":"2021","journal-title":"Data Brief"},{"key":"10.1016\/j.imavis.2026.106077_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2024.104123","article-title":"Low-contrast X-ray image defect segmentation via a novel core-profile decomposition network","volume":"161","author":"Liu","year":"2024","journal-title":"Comput. Ind."},{"key":"10.1016\/j.imavis.2026.106077_b38","first-page":"29","article-title":"Welding defects classification through a Convolutional Neural Network","volume":"35","author":"Perri","year":"2023","journal-title":"Manuf. Lett."}],"container-title":["Image and Vision Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0262885626001848?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0262885626001848?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T06:24:06Z","timestamp":1784528646000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0262885626001848"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":38,"alternative-id":["S0262885626001848"],"URL":"https:\/\/doi.org\/10.1016\/j.imavis.2026.106077","relation":{},"ISSN":["0262-8856"],"issn-type":[{"value":"0262-8856","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"EMAN: Efficient feature modulation and aggregation network for defect classification in industrial images","name":"articletitle","label":"Article Title"},{"value":"Image and Vision Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.imavis.2026.106077","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106077"}}