Multi-dimensional feature extraction-based deep encoder-decoder network for automatic 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.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:53:03Z
dc.date.available2024-08-04T20:53:03Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe control of surface defects is of critical importance in manufacturing quality control systems. Today, automatic defects detection using imaging and deep learning algorithms has produced more successful results than manual inspections. Thanks to these automatic applications, manufacturing systems will increase the production quality, and thus financial losses will be prevented. However, since the appearance and dimensions of the defects on the surface are very variable, automatic surface defect detection is a complex problem. In this study, multi-dimensional feature extraction-based deep encoder-decoder network (MFE-DEDNet) network developed to solve such problems. An effective encoder-decoder model with lower parameters compared to the state-of-the-art methods is developed using the depthwise separable convolutions (DSC) layers in the proposed model. In addition, the 3D spectral and spatial features extract (3DFE) module of the proposed model is developed to extract deep spectral and spatial features, as well as deep semantic features. During the combination of these features, the multi-input attention gate (MIAG) module is used so that important details are not lost. As a result, the proposed MFE-DEDNet model based on these structures enabled the extraction of powerful and effective features for defect detection in surface datasets containing few images. In experimental studies, MVTec and MT datasets were used to evaluate the performance of the MFE-DEDNet. The experimental results achieved 80.01% and 56% mean intersection-over-union (mIoU) scores for the MT and MVTec datasets, respectively. In these results, it was observed that the proposed model produced higher success compared to other state-of-the-art methods.en_US
dc.identifier.doi10.1007/s00521-022-07885-z
dc.identifier.endpage3282en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85139675057en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3263en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-022-07885-z
dc.identifier.urihttps://hdl.handle.net/11616/100936
dc.identifier.volume35en_US
dc.identifier.wosWOS:000865154800002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSurface defect detectionen_US
dc.subjectDeep learningen_US
dc.subject3D spectral and spatial features extracten_US
dc.subjectEncoder-decoderen_US
dc.titleMulti-dimensional feature extraction-based deep encoder-decoder network for automatic surface defect detectionen_US
dc.typeArticleen_US

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