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Article: Automated fabric defect detection-A review

TitleAutomated fabric defect detection-A review
Authors
KeywordsAutomation
Fabric defect detection
Manufacturing
Motif-based
Quality control
Textile
Issue Date2011
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/imavis
Citation
Image And Vision Computing, 2011, v. 29 n. 7, p. 442-458 How to Cite?
AbstractThis paper provides a review of automated fabric defect detection methods developed in recent years. Fabric defect detection, as a popular topic in automation, is a necessary and essential step of quality control in the textile manufacturing industry. In categorizing these methods broadly, a major group is regarded as non-motif-based while a minor group is treated as motif-based. Non-motif-based approaches are conventional, whereas the motif-based approach is novel in utilizing motif as a basic manipulation unit. Compared with previously published review papers on fabric inspection, this paper firstly offers an up-to-date survey of different defect detection methods and describes their characteristics, strengths and weaknesses. Secondly, it employs a wider classification of methods and divides them into seven approaches (statistical, spectral, model-based, learning, structural, hybrid, and motif-based) and performs a comparative study across these methods. Thirdly, it also presents a qualitative analysis accompanied by results, including detection success rate for every method it has reviewed. Lastly, insights, synergy and future research directions are discussed. This paper shall benefit researchers and practitioners alike in image processing and computer vision fields in understanding the characteristics of the different defect detection approaches. © 2011 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/135101
ISSN
2015 Impact Factor: 1.766
2015 SCImago Journal Rankings: 1.700
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorNgan, HYTen_HK
dc.contributor.authorPang, GKHen_HK
dc.contributor.authorYung, NHCen_HK
dc.date.accessioned2011-07-27T01:28:20Z-
dc.date.available2011-07-27T01:28:20Z-
dc.date.issued2011en_HK
dc.identifier.citationImage And Vision Computing, 2011, v. 29 n. 7, p. 442-458en_HK
dc.identifier.issn0262-8856en_HK
dc.identifier.urihttp://hdl.handle.net/10722/135101-
dc.description.abstractThis paper provides a review of automated fabric defect detection methods developed in recent years. Fabric defect detection, as a popular topic in automation, is a necessary and essential step of quality control in the textile manufacturing industry. In categorizing these methods broadly, a major group is regarded as non-motif-based while a minor group is treated as motif-based. Non-motif-based approaches are conventional, whereas the motif-based approach is novel in utilizing motif as a basic manipulation unit. Compared with previously published review papers on fabric inspection, this paper firstly offers an up-to-date survey of different defect detection methods and describes their characteristics, strengths and weaknesses. Secondly, it employs a wider classification of methods and divides them into seven approaches (statistical, spectral, model-based, learning, structural, hybrid, and motif-based) and performs a comparative study across these methods. Thirdly, it also presents a qualitative analysis accompanied by results, including detection success rate for every method it has reviewed. Lastly, insights, synergy and future research directions are discussed. This paper shall benefit researchers and practitioners alike in image processing and computer vision fields in understanding the characteristics of the different defect detection approaches. © 2011 Elsevier B.V. All rights reserved.en_HK
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/imavisen_HK
dc.relation.ispartofImage and Vision Computingen_HK
dc.subjectAutomationen_HK
dc.subjectFabric defect detectionen_HK
dc.subjectManufacturingen_HK
dc.subjectMotif-baseden_HK
dc.subjectQuality controlen_HK
dc.subjectTextileen_HK
dc.titleAutomated fabric defect detection-A reviewen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0262-8856&volume=29&issue=7&spage=442&epage=458&date=2011&atitle=Automated+fabric+defect+detection:+a+review-
dc.identifier.emailPang, GKH:gpang@eee.hku.hken_HK
dc.identifier.emailYung, NHC:nyung@eee.hku.hken_HK
dc.identifier.authorityPang, GKH=rp00162en_HK
dc.identifier.authorityYung, NHC=rp00226en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.imavis.2011.02.002en_HK
dc.identifier.scopuseid_2-s2.0-79957788287en_HK
dc.identifier.hkuros186794en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79957788287&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume29en_HK
dc.identifier.issue7en_HK
dc.identifier.spage442en_HK
dc.identifier.epage458en_HK
dc.identifier.isiWOS:000292344800002-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridNgan, HYT=7102173824en_HK
dc.identifier.scopusauthoridPang, GKH=7103393283en_HK
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK
dc.identifier.citeulike9174267-

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