Automated fabric inspection system development aided with convolutional autoencoder-based defect detection

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dc.contributor.authorMercimek, Muharrem
dc.contributor.authorÖz, Muhammed Ali Nur
dc.contributor.authorKaymakçı, Özgür Turay
dc.date.accessioned2025-01-27T19:28:38Z
dc.date.available2025-01-27T19:28:38Z
dc.date.issued2024
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractIndustrial automatic fabric inspection system, a critical technology in the industry, enhances both total production quantity and quality compared to conventional inspection techniques. This study aims to create a reliable and effective real-time automated visual inspection system for fabrics, focusing on defect detection. The goals of the study can be stated as; installing a system with advanced technology for capturing and processing images swiftly, the development and deployment of a system capable of autonomously learning and scanning fabrics in use, and the creation of a smart framework for accurate fabric defect detection and classification. We focus on the development of unsupervised fabric defect detection using a convolutional autoencoder model, and defect classification using a convolutional neural network model, which takes input as the feature vector generated by the convolutional autoencoder. The experimental outcomes have displayed significant success rates in both detecting defects and classifying them, confirming the effectiveness of the framework in real-time visual inspection systems.
dc.identifier.doi10.28948/ngumuh.1481769
dc.identifier.endpage1114
dc.identifier.issn2564-6605
dc.identifier.issue4
dc.identifier.startpage1100
dc.identifier.trdizinid1272477
dc.identifier.urihttps://doi.org/10.28948/ngumuh.1481769
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1272477
dc.identifier.urihttps://hdl.handle.net/20.500.12428/15825
dc.identifier.volume13
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofNiğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TRD_20250125
dc.subjectFabric defect detection
dc.subjectFabric inspection system
dc.subjectConvolutional autoencoder
dc.titleAutomated fabric inspection system development aided with convolutional autoencoder-based defect detection
dc.typeArticle

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