Fuzzy logic-based segmentation of manufacturing defects on reflective surfaces

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dc.contributor.authorOzturk, Saban
dc.contributor.authorAkdemir, Bayram
dc.date.accessioned2020-03-26T19:54:03Z
dc.date.available2020-03-26T19:54:03Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractAutomatic defect detection on reflective surfaces is a compelling process. In particular, detection of tiny defects is almost impossible for human eye or simple machine vision methods. Therefore, the need for fast and sensitive machine vision methods has gained importance. In this study, an effective defect detection method is presented for reflective surfaces such as glass, tile, and steel. Defects on the surface of the product are determined automatically without the need for human intervention. The proposed system involves illumination unit, digital camera, and defect detection algorithm. Firstly, color image is taken by digital camera. Then, properties of taken image are selected. At this stage, ambient condition of lighting devices is very important. Reflections are minimized thanks to the true lighting. Selected properties are: red, green, and blue values, and luminance value. These properties are applied to fuzzy inputs. Information from the inputs is evaluated according to determined rules. Finally, each pixel is classified as black or white. Thirty-two glass pieces are tested using the proposed system. The proposed method was compared with commonly used methods. The success rate of the proposed algorithm is 83.5% and is higher than that of other algorithms .en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [114E925]en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) with Project Number: 114E925.en_US
dc.identifier.doi10.1007/s00521-017-2862-6en_US
dc.identifier.endpage116en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage107en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-017-2862-6
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36651
dc.identifier.volume29en_US
dc.identifier.wosWOS:000427799900009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER LONDON LTDen_US
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectDefect detectionen_US
dc.subjectReflective surface analysisen_US
dc.subjectGlass defect inspectionen_US
dc.subjectFuzzy logicen_US
dc.subjectTexture analysisen_US
dc.subjectImage segmentationen_US
dc.titleFuzzy logic-based segmentation of manufacturing defects on reflective surfacesen_US
dc.typeArticleen_US

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