Classification of Circular Knitting Fabric Defects Using MobileNetV2 Model

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dc.contributor.authorHanbay, Kazım
dc.date.accessioned2024-08-04T19:54:40Z
dc.date.available2024-08-04T19:54:40Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractFabric defects cause both labor and raw material losses and energy costs. These undesirable situations negatively affect the competitiveness of companies in the textile sector. Traditionally, human-oriented quality control also has important limitations such as lack of attention and fatigue. Robust and efficient defect detection systems can be developed with image processing and artificial intelligence methods. This study proposes a deep learning-based method to detect and classify common fabric defects in circular knitting fabrics. The proposed method adds a fine-tuned mechanism to the MobileNetV2 deep learning model. The added fine-tuned mechanism is optimized to classify fabric defects. The proposed model has been tested on a fabric dataset containing circular knitting fabric defects. Obtained results showed that the proposed method produced desired results in fabric defect detection and classification.en_US
dc.identifier.doi10.46810/tdfd.1327971
dc.identifier.endpage68en_US
dc.identifier.issn2149-6366
dc.identifier.issue4en_US
dc.identifier.startpage63en_US
dc.identifier.trdizinid1220773en_US
dc.identifier.urihttps://doi.org/10.46810/tdfd.1327971
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1220773
dc.identifier.urihttps://hdl.handle.net/11616/90041
dc.identifier.volume12en_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.titleClassification of Circular Knitting Fabric Defects Using MobileNetV2 Modelen_US
dc.typeArticleen_US

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