Novel BiasFeed Cellular Neural Network Model for Glass Defect Inspection

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dc.contributor.authorOzturk, Saban
dc.contributor.authorAkdemir, Bayram
dc.date.accessioned2020-03-26T19:25:23Z
dc.date.available2020-03-26T19:25:23Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesien_US
dc.description3rd International Conference on Control, Decision and Information Technologies (CoDIT) -- APR 06-08, 2016 -- St Pauls Bay, MALTAen_US
dc.description.abstractIn this study, an effective segmentation method is presented for defect detection on the glass surface. Defect detection on the glass surface is compelling and strenuous job for human eyes. Transparency and reflection properties of the glass surface reduce success of image processing algorithms using detection of the factors that unwanted and affecting quality of products such as crack, scratch, bubble. Traditional methods have limited success and long processing time in this process. Therefore, fast and effective method has been proposed. In the proposed method (BiasFeed CNN), bias input which is single number value traditional CNN algorithm is converted bias template. Bias template is used to balance the brightness level of the image. Input image and bias template convolution is applied bias input. Through the contribution from bias input, background reflections and negative effects arising from transparency are decreased. The developed method is fast as traditional CNN, because it does not cause significant changes in the structure of traditional CNN. 35 pieces of glass was tested using the algorithm. Damages in the glass surface and location of these damages were determined. Accuracy rates of inspected images are; sensivity % 91, specificity % 99, accuracy % 98. BiasFeed CNN algorithm was tested on various images and it is more successful than traditional CNN algorithm.en_US
dc.description.sponsorshipIEEE, Univ Malta, IEEE Syst Man & Cybernet Soc, IEEE Malta Sect, IEEE Robot & Automat Soc, Tunisia Chapter, Int Inst Innovat Ind Engn & Entrepreneurship, GDR RO, GDR MACSen_US
dc.identifier.endpage371en_US
dc.identifier.isbn978-1-5090-2188-8
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage366en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33836
dc.identifier.wosWOS:000386533900066en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2016 INTERNATIONAL CONFERENCE ON CONTROL, DECISION AND INFORMATION TECHNOLOGIES (CODIT)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectcellular neural networken_US
dc.subjectbiasfeeden_US
dc.subjectglass defect detectionen_US
dc.subjecttexture inspectionen_US
dc.subjectcnnen_US
dc.titleNovel BiasFeed Cellular Neural Network Model for Glass Defect Inspectionen_US
dc.typeConference Objecten_US

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