{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:26:06Z","timestamp":1783124766445,"version":"3.54.6"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005047","name":"Liaoning Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["23-503-6-02"],"award-info":[{"award-number":["23-503-6-02"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016807","name":"Natural Science Foundation of Shenyang Municipality","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N25QSX007"],"award-info":[{"award-number":["N25QSX007"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N2303011"],"award-info":[{"award-number":["N2303011"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52275091"],"award-info":[{"award-number":["52275091"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.engappai.2026.115148","type":"journal-article","created":{"date-parts":[[2026,5,17]],"date-time":"2026-05-17T17:45:39Z","timestamp":1779039939000},"page":"115148","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["A gear surface defect detection approach based on class-linked mechanism"],"prefix":"10.1016","volume":"178","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6984-594X","authenticated-orcid":false,"given":"Shihua","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zichun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wentao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaibo","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingshuo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaohui","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.115148_bib1","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107717","article-title":"A systematic review of deep learning approaches for surface defect detection in industrial applications","volume":"130","author":"Ameri","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115148_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107697","article-title":"Surface defect detection methods for industrial products with imbalanced samples: a review of progress in the 2020s","volume":"130","author":"Bai","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.115148_bib3","series-title":"Yolov4: Optimal Speed and Accuracy of Object Detection","author":"Bochkovskiy","year":"2020"},{"key":"10.1016\/j.engappai.2026.115148_bib4","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2020.3040485","article-title":"RetinaNet with difference channel attention and adaptively spatial feature fusion for steel surface defect detection","volume":"70","author":"Cheng","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115148_bib5","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.113374","article-title":"A novel multi-model cascade framework for pipeline defects detection based on machine vision","volume":"220","author":"Gao","year":"2023","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115148_bib6","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ad5dd5","article-title":"ODCS-YOLO detection algorithm for rail surface defects based on omni-dimensional dynamic convolution and context augmentation module","volume":"35","author":"Gao","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.115148_bib7","first-page":"1440","article-title":"Faster R-CNN","author":"Girshick","year":"2015","journal-title":"IEEE Int. Conf. Comput. Vis."},{"key":"10.1016\/j.engappai.2026.115148_bib8","series-title":"Fast R-CNN","first-page":"1440","author":"Girshick","year":"2015"},{"key":"10.1016\/j.engappai.2026.115148_bib9","series-title":"2014 IEEE Conf. Comput. Vision Pattern Recogn","first-page":"580","article-title":"Rich feature hierarchies for accurate object detection and semantic segmentation","author":"Girshick","year":"2014"},{"key":"10.1016\/j.engappai.2026.115148_bib10","doi-asserted-by":"crossref","DOI":"10.1016\/j.optlastec.2023.110344","article-title":"Online defect detection method of optical cable pitch based on machine vision technology and deep learning algorithms","volume":"171","author":"Gou","year":"2024","journal-title":"Opt. Laser Technol."},{"key":"10.1016\/j.engappai.2026.115148_bib11","series-title":"IEEE Int. Conf. Comput. Vision","article-title":"Mask R-CNN","author":"He","year":"2017"},{"key":"10.1016\/j.engappai.2026.115148_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2025.106437","article-title":"A comprehensive review of research on surface defect detection of PCBs based on machine vision","volume":"27","author":"He","year":"2025","journal-title":"Results Eng."},{"key":"10.1016\/j.engappai.2026.115148_bib13","doi-asserted-by":"crossref","first-page":"351","DOI":"10.3390\/solar4030016","article-title":"In-depth review of yolov1 to yolov10 variants for enhanced photovoltaic defect detection","volume":"4","author":"Hussain","year":"2024","journal-title":"Solar"},{"issue":"1","key":"10.1016\/j.engappai.2026.115148_bib14","doi-asserted-by":"crossref","first-page":"35954","DOI":"10.1109\/JSEN.2024.3454311","article-title":"CNCR-YOLO: a comprehensive optimization strategy for small-target defect detection in injection-molded parts","volume":"24","author":"Jiang","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.engappai.2026.115148_bib15","first-page":"522","article-title":"Local regression based real-time traffic sign detection using YOLOv6","author":"Kaur","year":"2022","journal-title":"4th Int. Conf. Adv. Comput."},{"key":"10.1016\/j.engappai.2026.115148_bib16","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.mechmachtheory.2015.05.013","article-title":"Method for remanufacturing large sized skew bevel gears using CNC machining center","volume":"92","author":"Kawasaki","year":"2015","journal-title":"Mech. Mach. Theor."},{"key":"10.1016\/j.engappai.2026.115148_bib17","unstructured":"Khanam, R., Hussain, M., 2024. YOLOV11: an overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725v1."},{"key":"10.1016\/j.engappai.2026.115148_bib18","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.ijtst.2024.06.006","article-title":"A systematic literature review of defect detection in railways using machine vision-based inspection methods","volume":"18","author":"Kumar","year":"2025","journal-title":"Int. J. Transp. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.115148_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106466","article-title":"A weak supervision machine vision detection method based on artificial defect simulation","volume":"208","author":"Li","year":"2020","journal-title":"Knowl Based Syst."},{"key":"10.1016\/j.engappai.2026.115148_bib20","article-title":"YOLOV6: a single-stage object detection framework for industrial applications","author":"Li","year":"2022","journal-title":"arXiv:2209"},{"issue":"7","key":"10.1016\/j.engappai.2026.115148_bib21","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.egyr.2022.08.027","article-title":"Insulator defect detection for power grid based on light correction enhancement and YOLOv5 model","volume":"8","author":"Li","year":"2022","journal-title":"Energy Rep."},{"key":"10.1016\/j.engappai.2026.115148_bib22","first-page":"1","article-title":"An efficient advanced-YOLOv8 framework for THz object detection","volume":"73","author":"Li","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115148_bib23","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1016\/j.ymssp.2017.05.024","article-title":"Dynamic modeling of gearbox faults: a review","volume":"98","author":"Liang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.115148_bib24","series-title":"SSD: Single Shot Multibox Detector","first-page":"21","author":"Liu","year":"2016"},{"key":"10.1016\/j.engappai.2026.115148_bib25","series-title":"SSD: Single Shot Multibox Detector","first-page":"21","author":"Liu","year":"2016"},{"key":"10.1016\/j.engappai.2026.115148_bib26","article-title":"Improved YOLOv7 based on reparameterization and attention mechanism for continuous casting slab detection","volume":"74","author":"Liu","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115148_bib27","article-title":"SFC-YOLOv8: enhanced strip steel surface defect detection using spatial-frequency domain-optimized YOLOv8","volume":"74","author":"Liu","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115148_bib28","first-page":"1","article-title":"Improved YOLOv7 based on reparameterization and attention mechanism for continuous casting slab detection","volume":"74","author":"Liu","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115148_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.118680","article-title":"Multi-scale defect detection method for involute gear tooth flank in the context of remanufacturing","volume":"257","author":"Liu","year":"2026","journal-title":"Measurement"},{"issue":"3","key":"10.1016\/j.engappai.2026.115148_bib30","first-page":"244","article-title":"Research on surface defect detection of steel plate based on improved faster RCNN","volume":"230","author":"Lu","year":"2024","journal-title":"Print. Digital Media Tech. Stud."},{"key":"10.1016\/j.engappai.2026.115148_bib31","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2025.3563011","article-title":"SGT-YOLO: a lightweight method for pcb defect detection","volume":"74","author":"Mo","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115148_bib32","article-title":"Yolov3: an incremental improvement","author":"Redmon","year":"2018","journal-title":"arXiv preprint arXiv: 1804.02767"},{"key":"10.1016\/j.engappai.2026.115148_bib33","series-title":"2016 IEEE Conf. Comput. Vision Pattern Recognition (CVPR)","first-page":"779","article-title":"You only look once: unified, real-time object detection","author":"Redmon","year":"2016"},{"issue":"6","key":"10.1016\/j.engappai.2026.115148_bib34","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE T. Pattern Anal."},{"key":"10.1016\/j.engappai.2026.115148_bib35","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/ad9e25","article-title":"Surface defect detection of steel strip at low resolution based on SAC-YOLOv5","volume":"36","author":"Rui","year":"2025","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.115148_bib36","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1016\/j.measurement.2011.09.005","article-title":"Amended bacterial foraging algorithm for multilevel thresholding of magnetic resonance brain images","volume":"44","author":"Sathya","year":"2011","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115148_bib37","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.measurement.2014.10.009","article-title":"Detection and classification of surface defects of gun barrels using computer vision and machine learning","volume":"60","author":"Shanmugamani","year":"2015","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115148_bib38","doi-asserted-by":"crossref","first-page":"73889","DOI":"10.1109\/ACCESS.2025.3564288","article-title":"Peduncle detection of ripe strawberry to localize picking point using DF-Mask R-CNN and monocular depth","volume":"13","author":"Tamrakar","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.115148_bib39","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2023.3250302","article-title":"LFRNet: localizing, focus, and refinement network for salient object detection of surface defects","volume":"72","author":"Wan","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115148_bib40","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1016\/j.measurement.2012.10.023","article-title":"A measurement method for contact angle based on Hough transformation","volume":"46","author":"Xu","year":"2013","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115148_bib41","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.114970","article-title":"ESMNet: an enhanced YOLOv7-based approach to detect surface defects in precision metal workpieces","volume":"235","author":"Xu","year":"2024","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115148_bib42","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.113619","article-title":"Generalized weld bead region of interest localization and improved faster R-CNN for weld defect recognition","volume":"222","author":"Yang","year":"2023","journal-title":"Measurement"},{"issue":"13","key":"10.1016\/j.engappai.2026.115148_bib43","doi-asserted-by":"crossref","first-page":"21762","DOI":"10.1109\/JSEN.2024.3403870","article-title":"SF-YOLO: an evolutionary deep neural network for gear end surface defect detection","volume":"24","author":"Yang","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.engappai.2026.115148_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.109683","article-title":"A machine vision method for measurement of machining tool wear","volume":"182","author":"Yu","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115148_bib45","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.109248","article-title":"A vision-based fusion method for defect detection of milling cutter spiral cutting edge","volume":"177","author":"Zhang","year":"2021","journal-title":"Measurement"},{"key":"10.1016\/j.engappai.2026.115148_bib46","article-title":"Adaptive defect detection for 3-D printed lattice structures based on improved faster R-CNN","volume":"71","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115148_bib47","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2022.3194909","article-title":"FINet: an insulator dataset and detection benchmark based on synthetic fog and improved YOLOv5","volume":"71","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.115148_bib48","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6501\/acf9bf","article-title":"A real-time method for detecting bottom defects of lithium batteries based on an improved YOLOv5 model","volume":"34","author":"Zhang","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.115148_bib49","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.113433","article-title":"An industrial interference-resistant gear defect detection method through improved YOLOv5 network using attention mechanism and feature fusion","volume":"221","author":"Zhang","year":"2023","journal-title":"Measurement"},{"issue":"16","key":"10.1016\/j.engappai.2026.115148_bib50","doi-asserted-by":"crossref","first-page":"26935","DOI":"10.1109\/JSEN.2024.3419806","article-title":"Improved YOLOv8-CR network for detecting defects of the automotive MEMS pressure sensors","volume":"24","author":"Zhang","year":"2024","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.engappai.2026.115148_bib51","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.113472","article-title":"A small sample nonstandard gear surface defect detection method","volume":"221","author":"Zhou","year":"2023","journal-title":"Measurement"},{"issue":"15","key":"10.1016\/j.engappai.2026.115148_bib52","doi-asserted-by":"crossref","first-page":"30020","DOI":"10.1109\/JSEN.2025.3581717","article-title":"GSD-YOLO: a gear surface defects detection method using adaptive multiscale fusion and hybrid feature fusion","volume":"25","author":"Zhou","year":"2025","journal-title":"IEEE Sens. J."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626014314?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626014314?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T00:10:58Z","timestamp":1783123858000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626014314"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":52,"alternative-id":["S0952197626014314"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.115148","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A gear surface defect detection approach based on class-linked 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.2026.115148","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":"115148"}}