{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T13:32:07Z","timestamp":1779024727227,"version":"3.51.4"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976028"],"award-info":[{"award-number":["61976028"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["KYCX22_3068"],"award-info":[{"award-number":["KYCX22_3068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3374131","type":"journal-article","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T19:15:20Z","timestamp":1709666120000},"page":"36457-36465","source":"Crossref","is-referenced-by-count":5,"title":["A Method for Surface Defect Detection Based on Multiscale Feature Fusion and Pyramid Attention"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4751-4137","authenticated-orcid":false,"given":"Ying","family":"Tang","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1236-6141","authenticated-orcid":false,"given":"Hongyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qunying","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boyan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-023-2764-y"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-023-3394-0"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3214430"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413763"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611744"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2022.026943"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3390\/s20185136"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/app12020834"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3067221"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICUMT57764.2022.9943348"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_7"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2020.103232"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/icpr48806.2021.9412092"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103459"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2022.103834"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2012.6252468"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966162"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-017-0882-0"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2905905"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3390\/s20071974"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3083561"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3246519"},{"key":"ref24","first-page":"1","article-title":"Pushing the limits of fewshot anomaly detection in industry vision: Graphcore","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Xie"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00195"},{"key":"ref26","first-page":"128","article-title":"EfficientAD: Accurate visual anomaly detection at millisecond-level latencies","volume-title":"Proc. IEEE\/CVF Winter Conf. Appl. Comput. Vis.","author":"Batzner"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045336"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref29","first-page":"1","article-title":"Gather-excite: Exploiting feature context in convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Hu"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1807.06521"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2867261"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01152"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-26313-2_33"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3297408"},{"key":"ref39","first-page":"1","article-title":"Gold- YOLO: Efficient object detector via gather-and-distribute mechanism","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Wang"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3206108"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-019-01476-x"},{"key":"ref42","volume-title":"Weakly supervised learning for industrial optical inspection","author":"Wieler","year":"2007"},{"key":"ref43","volume-title":"Severstal: Steel Defect Detection","author":"Grishin","year":"2019"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.01.010"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"ref46","article-title":"Self-supervised guided segmentation framework for unsupervised anomaly detection","author":"Xing","year":"2022","journal-title":"arXiv:2209.12440"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.3390\/machines9100221"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19821-2_31"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/10460557.pdf?arnumber=10460557","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T11:35:39Z","timestamp":1711452939000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10460557\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3374131","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}