Texture defect classification with multiple pooling and filter ensemble based on deep neural network

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dc.authoridUZEN, Huseyin/0000-0002-0998-2130
dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authorwosidUZEN, Huseyin/CZK-0841-2022
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.contributor.authorUzen, Huseyin
dc.contributor.authorTurkoglu, Muammer
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:49:24Z
dc.date.available2024-08-04T20:49:24Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractFabric quality control is one of the most important phases of production in order to ensure high-quality standards in the fabric production sector. For this reason, the development of successful automatic quality control systems has been a very important research subject. In this study, we propose a Multiple Pooling and Filter approach based on a Deep Neural Network (MPF-DNN) for the classification of texture defects. This model consists of three basic stages: preprocessing, feature extraction, and classification. In the preprocessing stage, the texture images were first divided into n x n equal parts. Then, median filtering and pooling processes were applied to each piece prior to performing image merging. In the proposed pre-treatment stage, it is aimed to clarify fabric errors and increase performance. For the feature extraction stage, deep features were extracted from the texture images using the pretrained ResNet101 model based on the transfer learning approach. Finally, classification and testing procedures were conducted on the obtained deep-effective properties using the SVM method. The multiclass TILDA dataset was used in order to test the proposed model. In experimental work, the MPF-DNN model for all four classes achieved a significant overall accuracy score of 95.82%. In the results obtained from extensive experimental studies, it was observed that the proposed MPF-DNN model was more successful than previous studies that used pretrained deep architectures.en_US
dc.identifier.doi10.1016/j.eswa.2021.114838
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85102632764en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2021.114838
dc.identifier.urihttps://hdl.handle.net/11616/99824
dc.identifier.volume175en_US
dc.identifier.wosWOS:000664351700035en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTexture defect recognitionen_US
dc.subjectDeep featuresen_US
dc.subjectSupport vector machineen_US
dc.subjectData augmenten_US
dc.subjectClassificationen_US
dc.titleTexture defect classification with multiple pooling and filter ensemble based on deep neural networken_US
dc.typeArticleen_US

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