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Reducing False Detection during Inspection of HDD using Super Resolution Image Processing and Deep Learning

dc.contributor.authorIeamsaard, Jirarat
dc.contributor.authorSandnes, Frode Eika
dc.contributor.authorMuneesawang, Paisarn
dc.date.accessioned2017-09-10T17:51:14Z
dc.date.accessioned2017-09-15T06:59:55Z
dc.date.available2017-09-10T17:51:14Z
dc.date.available2017-09-15T06:59:55Z
dc.date.issued2017
dc.identifier.citationIeamsaard J, Sandnes FE, Muneesawang P. Reducing False Detection during Inspection of HDD using Super Resolution Image Processing and Deep Learning. Journal of Telecommunication, Electronic and Computer Engineering. 2017;9(2-5):91-95language
dc.identifier.issn2180-1843
dc.identifier.urihttps://hdl.handle.net/10642/5225
dc.description.abstractHigh false detection rates are a key reliability challenge in the Hard Disk Drive (HDD) industry. Therefore, automatic visual inspection is increasingly employed for HDD inspection. In order to improve the quality and reliability of HDD products, the false detection rate must be reduced. This paper presents a super - resolution image - based method for improving the performance of Head Gimbals Assembly (HGA) inspectio n. The experimental results confirm the efficiency of the super - resolution image processing for improving automatic inspection of defects such as pad burning and micro contaminations. Moreover, combining super resolution image processing with deep learning reduces the false detection rate and improves the accuracy of HGA inspection.language
dc.language.isoenlanguage
dc.publisherUniversiti Teknikal Malaysia Melakalanguage
dc.rightsThis work is licensed under a Creative Commons Attribution 3.0 License.language
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/
dc.subjectImage resolutionlanguage
dc.subjectHGA inspectionlanguage
dc.subjectContamination detectionlanguage
dc.subjectHDDlanguage
dc.titleReducing False Detection during Inspection of HDD using Super Resolution Image Processing and Deep Learninglanguage
dc.typeJournal articlelanguage
dc.typePeer reviewedlanguage
dc.date.updated2017-09-10T17:51:14Z
dc.description.versionpublishedVersionlanguage
dc.identifier.cristin1492492
dc.source.journalJournal of Telecommunication, Electronic and Computer Engineering


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