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dc.contributor.authorLuo, Qiwu
dc.contributor.authorFang, Xiaoxin
dc.contributor.authorLiu, Li
dc.contributor.authorYang, Chunhua
dc.contributor.authorSun, Yichuang
dc.date.accessioned2020-01-07T01:07:53Z
dc.date.available2020-01-07T01:07:53Z
dc.date.issued2020-01-01
dc.identifier.citationLuo , Q , Fang , X , Liu , L , Yang , C & Sun , Y 2020 , ' Automated Visual Defect Detection for Flat Steel Surface: A Survey ' , IEEE Transactions on Instrumentation and Measurement , pp. 1-18 . https://doi.org/10.1109/TIM.2019.2963555
dc.identifier.issn0018-9456
dc.identifier.otherPURE: 18310778
dc.identifier.otherPURE UUID: 6b54b77e-d1be-46dc-a5dd-532888d32d76
dc.identifier.urihttp://hdl.handle.net/2299/22040
dc.description© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractAutomated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot- and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.en
dc.format.extent18
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Instrumentation and Measurement
dc.rightsOpen
dc.titleAutomated Visual Defect Detection for Flat Steel Surface: A Surveyen
dc.contributor.institutionCentre for Engineering Research
dc.contributor.institutionCommunications and Intelligent Systems
dc.contributor.institutionSchool of Engineering and Computer Science
dc.contributor.institutionDepartment of Engineering and Technology
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Engineering and Computer Science
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2020-01-01
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1109/TIM.2019.2963555
rioxxterms.licenseref.uriOther
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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