{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T02:45:48Z","timestamp":1786243548887,"version":"build-2736575974"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.engappai.2026.115674","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T12:53:31Z","timestamp":1783947211000},"page":"115674","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P5","title":["Metal surface defect detection model based on multi-scale feature fusion and attention mechanism"],"prefix":"10.1016","volume":"181","author":[{"given":"Rui","family":"Ni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanning","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siwen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yelin","family":"Xia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maowei","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115674_bib1","series-title":"2017 International Artificial Intelligence and Data Processing Symposium (IDAP)","first-page":"1","article-title":"Machine vision based defect detection approach using image processing","author":"Baygin","year":"2017"},{"key":"10.1016\/j.engappai.2026.115674_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109325","article-title":"An efficient detector for detecting surface defects on cold-rolled steel strips","volume":"138","author":"Chen","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115674_bib3","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.7717\/peerj-cs.1727","article-title":"Unleashing the power of AI in detecting metal surface defects: an optimized YOLOv7-tiny model approach","volume":"10","author":"Chen","year":"2024","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.engappai.2026.115674_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105029","article-title":"Unleashing breakthroughs in aluminum surface defect detection: advancing precision with an optimized YOLOv8n model","volume":"160","author":"Chen","year":"2025","journal-title":"Digit. Signal Process."},{"key":"10.1016\/j.engappai.2026.115674_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2021.103530","article-title":"Ensemble of convolutional neural networks based on an evolutionary algorithm applied to an industrial welding process","volume":"133","author":"Cruz","year":"2021","journal-title":"Comput. Ind."},{"issue":"6","key":"10.1016\/j.engappai.2026.115674_bib6","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.ndteint.2003.12.004","article-title":"Pattern recognition of weld defects detected by radiographic test","volume":"37","author":"da Silva","year":"2004","journal-title":"NDT&E Int."},{"key":"10.1016\/j.engappai.2026.115674_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.jallcom.2023.169215","article-title":"Pore defects in laser powder bed fusion: formation mechanism, control method, and perspectives","volume":"944","author":"Du","year":"2023","journal-title":"J. Alloys Compd."},{"key":"10.1016\/j.engappai.2026.115674_bib8","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s00170-024-14069-7","article-title":"Exploring deep fully convolutional neural networks for surface defect detection in complex geometries","volume":"134","author":"Garc\u00eda Pe\u00f1a","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"10.1016\/j.engappai.2026.115674_bib9","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2021.810876","article-title":"Intelligent detection of steel defects based on improved split attention networks","volume":"9","author":"Hao","year":"2022","journal-title":"Front. Bioeng. Biotechnol."},{"key":"10.1016\/j.engappai.2026.115674_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.ndteint.2021.102565","article-title":"Detecting the defects of warm-sprayed Ti-6Al-4V coating using eddy current testing method","volume":"125","author":"He","year":"2022","journal-title":"NDT&E Int."},{"issue":"9","key":"10.1016\/j.engappai.2026.115674_bib10","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"10.1016\/j.engappai.2026.115674_bib11","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1109\/TIM.2019.2915404","article-title":"An end-to-end steel surface defect detection approach via fusing multiple hierarchical features","volume":"69","author":"He","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115674_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107007","article-title":"MLR-Net: a multi-layer residual convolutional neural network for leather defect segmentation","volume":"126","author":"Iqbal","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"18","key":"10.1016\/j.engappai.2026.115674_bib14","doi-asserted-by":"crossref","first-page":"3795","DOI":"10.3390\/electronics12183795","article-title":"Enhancement of product-inspection accuracy using convolutional neural network and laplacian filter to automate industrial manufacturing processes","volume":"12","author":"Jun","year":"2023","journal-title":"Electronics"},{"issue":"7","key":"10.1016\/j.engappai.2026.115674_bib15","doi-asserted-by":"crossref","first-page":"3761","DOI":"10.3390\/s23073761","article-title":"Defect detection for metal shaft surfaces based on an improved YOLOv5 algorithm and transfer learning","volume":"23","author":"Li","year":"2023","journal-title":"Sensors"},{"issue":"10","key":"10.1016\/j.engappai.2026.115674_bib17","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1177\/03019233241260922","article-title":"Research on strip surface defect detection based on improved YOLOv5 algorithm","volume":"51","author":"Lv","year":"2024","journal-title":"Ironmak. Steelmak."},{"issue":"6","key":"10.1016\/j.engappai.2026.115674_bib16","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.engappai.2026.115674_bib18","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s12289-019-01496-1","article-title":"Surface defect classification and detection on extruded aluminum profiles using convolutional neural networks","volume":"13","author":"Neuhauser","year":"2020","journal-title":"Int. J. Material Form."},{"issue":"3","key":"10.1016\/j.engappai.2026.115674_bib19","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s00138-024-01536-0","article-title":"Study on defect detection of metal castings based on supervised enhancement and attention distillation","volume":"35","author":"Pan","year":"2024","journal-title":"Mach. Vis. Appl."},{"key":"10.1016\/j.engappai.2026.115674_bib20","doi-asserted-by":"crossref","first-page":"48380","DOI":"10.1109\/ACCESS.2025.3544578","article-title":"A review of metal surface defect detection technologies in industrial applications","volume":"13","author":"Qiao","year":"2025","journal-title":"IEEE Access"},{"issue":"11","key":"10.1016\/j.engappai.2026.115674_bib21","doi-asserted-by":"crossref","first-page":"2598","DOI":"10.2355\/isijinternational.54.2598","article-title":"Surface defect detection method using saliency linear scanning morphology for silicon steel strip under oil pollution interference","volume":"54","author":"Song","year":"2014","journal-title":"ISIJ Int."},{"issue":"4","key":"10.1016\/j.engappai.2026.115674_bib22","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1109\/TCPMT.2018.2794540","article-title":"Wire defect recognition of spring-wire socket using multitask convolutional neural networks","volume":"8","author":"Tao","year":"2018","journal-title":"IEEE Trans. Compon. Packag. Manuf."},{"key":"10.1016\/j.engappai.2026.115674_bib25","doi-asserted-by":"crossref","first-page":"97758","DOI":"10.1109\/ACCESS.2022.3203198","article-title":"An enhanced YOLOv4 model with self-dependent attentive fusion and component randomized mosaic augmentation for metal surface defect detection","volume":"10","author":"Wang","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.115674_bib26","article-title":"A high-accuracy and lightweight detector based on a graph convolution network for strip surface defect detection","volume":"59","author":"Wang","year":"2023","journal-title":"Adv. Eng. Inform."},{"issue":"5","key":"10.1016\/j.engappai.2026.115674_bib23","doi-asserted-by":"crossref","first-page":"2798","DOI":"10.1109\/TII.2018.2887145","article-title":"A simple guidance template-based defect detection method for strip steel surfaces","volume":"15","author":"Wang","year":"2019","journal-title":"IEEE t. Ind. Inform."},{"key":"10.1016\/j.engappai.2026.115674_bib24","doi-asserted-by":"crossref","first-page":"43502","DOI":"10.1109\/ACCESS.2019.2908483","article-title":"Tire defect detection using fully convolutional network","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"issue":"6","key":"10.1016\/j.engappai.2026.115674_bib28","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.3390\/s25061720","article-title":"Surface defect detection based on adaptive multi-scale feature fusion","volume":"25","author":"Wen","year":"2025","journal-title":"Sensors"},{"issue":"13","key":"10.1016\/j.engappai.2026.115674_bib27","doi-asserted-by":"crossref","first-page":"5657","DOI":"10.3390\/app14135657","article-title":"Research on automated fiber placement surface defect detection based on improved YOLOv7","volume":"14","author":"Wen","year":"2024","journal-title":"Appl. Sci-Basel."},{"key":"10.1016\/j.engappai.2026.115674_bib29","series-title":"2019 International Conference on Image, Video and Signal Processing (IVSP)","first-page":"16","article-title":"Detection algorithm of aluminum surface defects using machine vision","author":"Wu","year":"2019"},{"issue":"6","key":"10.1016\/j.engappai.2026.115674_bib30","first-page":"12","article-title":"A high-effective multitask surface defect detection method based on CBAM and atrous convolution","volume":"16","author":"Xie","year":"2022","journal-title":"J. Adv. Mech. Des. Syst."},{"key":"10.1016\/j.engappai.2026.115674_bib31","first-page":"1","article-title":"Automatic detection and location of weld beads with deep convolutional neural networks","volume":"70","author":"Yang","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"10.1016\/j.engappai.2026.115674_bib33","doi-asserted-by":"crossref","first-page":"7671","DOI":"10.1038\/s41598-024-57990-3","article-title":"Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block","volume":"14","author":"Zhang","year":"2024","journal-title":"Sci. Rep-UK"},{"issue":"2","key":"10.1016\/j.engappai.2026.115674_bib32","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.jer.2024.02.007","article-title":"Image defect classification of surface mount technology welding based on the improved ResNet model","volume":"12","author":"Zhang","year":"2024","journal-title":"J. Eng. Res-Kuwait."},{"key":"10.1016\/j.engappai.2026.115674_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107134","article-title":"ICA-Net: industrial defect detection network based on convolutional attention and of multiscale features","volume":"126","author":"Zhao","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"20","key":"10.1016\/j.engappai.2026.115674_bib34","article-title":"An enhanced YOLOv5-Based algorithm for metal surface defect detection","volume":"13","author":"Zhao","year":"2023","journal-title":"Appl. Sci-Basel."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626019585?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626019585?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T02:33:59Z","timestamp":1786242839000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626019585"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":35,"alternative-id":["S0952197626019585"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115674","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Metal surface defect detection model based on multi-scale feature fusion and attention mechanism","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115674","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115674"}}