Fabric Defect Detection Using Customized Deep Convolutional Neural Network for Circular Knitting Fabrics

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dc.contributor.authorVarjovi, Mahdi Hatami
dc.contributor.authorTalu, Muhammed Fatih
dc.contributor.authorHanbay, Kazım
dc.date.accessioned2024-08-04T19:54:40Z
dc.date.available2024-08-04T19:54:40Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractVisual inspection is a main stage of quality assurance process in many applications. In this paper, we propose a new network architecture for detecting the fabric defects based on convolutional neural network. Four different pre-trained and customized model network architectures have compared in terms of performance. Results has been evaluated on a fabric defect dataset of 13.800 images. Among the existing Inception V3, MobileNetV2, Xception and ResNet50 methods, the InceptionV3 model has achieved 78% classification success. Our designed deep network model could achieve 97% success. The experimental works show that the designed deep model is effective in detecting the fabric defects.en_US
dc.identifier.doi10.46810/tdfd.1108264
dc.identifier.endpage165en_US
dc.identifier.issn2149-6366
dc.identifier.issue3en_US
dc.identifier.startpage160en_US
dc.identifier.trdizinid1127670en_US
dc.identifier.urihttps://doi.org/10.46810/tdfd.1108264
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1127670
dc.identifier.urihttps://hdl.handle.net/11616/90036
dc.identifier.volume11en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofTürk Doğa ve Fen Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleFabric Defect Detection Using Customized Deep Convolutional Neural Network for Circular Knitting Fabricsen_US
dc.typeArticleen_US

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