InceptionV3 based enriched feature integration network architecture for pixel-level surface defect detection

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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.authorAri, Ali
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:10:20Z
dc.date.available2024-08-04T20:10:20Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this study, InceptionV3 based Enriched Feature Integration Network (Inc-EFIN) architecture was developed for automatic detection of surface defects. In the proposed architecture, features of all levels of the InceptionV3 architecture are extracted and the features with the same height and width are combined. As a result of merging, 5 feature maps were obtained. Channel-Based Squeeze and Excitation block has been applied to reveal important details in these feature maps. In Feature Pyramid Network module, information from low-level feature maps containing spatial details were transferred to high-level feature maps containing semantic details. Then, for the final feature map, features were combined using the Feature Integration and Signification (FIS) module. The feature map combined in the FIS module was passed through the Spatial and Channel-based Squeeze and Excitation block. Defect detection results were obtained by using convolution and sigmoid layers in the last layer of the Inc-EFIN architecture. MT, MVTec-Texture, and DAGM datasets were used to calculate the pixel-level defect detection success of the Inc-EFIN architecture. In experimental studies, Inc-EFIN architecture achieved higher performance than the latest technologies in the literature with 77.44% mIoU, 81.2% mIoU and 79.46% mIoU performance results in MT, MVTec-Texture and DAGM datasets, respectively.en_US
dc.description.sponsorshipInonu University Scientific Research Projects [FDK-2021-2725]en_US
dc.description.sponsorshipThis study was supported by Inonu University Scientific Research Projects Coordination Unit (Project Number: FDK-2021-2725en_US
dc.identifier.doi10.17341/gazimmfd.1024425
dc.identifier.endpage732en_US
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85153891918en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage721en_US
dc.identifier.trdizinid1158761en_US
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.1024425
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1158761
dc.identifier.urihttps://hdl.handle.net/11616/92710
dc.identifier.volume38en_US
dc.identifier.wosWOS:000873967500008en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherGazi Univ, Fac Engineering Architectureen_US
dc.relation.ispartofJournal of The Faculty of Engineering and Architecture of Gazi Universityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPixel-level surface defects detectionen_US
dc.subjectconvolutional neural networken_US
dc.subjectsqueeze and excitation blocken_US
dc.subjectfeature pyramid networksen_US
dc.titleInceptionV3 based enriched feature integration network architecture for pixel-level surface defect detectionen_US
dc.typeArticleen_US

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