On combining convolutional autoencoders and support vector machines for fault detection in industrial textures

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dc.contributor.authorTellaeche Iglesias, Alberto
dc.contributor.authorCampos Anaya, Miguel Ángel
dc.contributor.authorPajares Martinsanz, Gonzalo
dc.contributor.authorPastor López, Iker
dc.date.accessioned2025-08-08T11:16:13Z
dc.date.available2025-08-08T11:16:13Z
dc.date.issued2021-05-02
dc.date.updated2025-08-08T11:16:13Z
dc.description.abstractDefects in textured materials present a great variability, usually requiring ad‐hoc solutions for each specific case. This research work proposes a solution that combines two machine learning-based approaches, convolutional autoencoders, CA; one class support vector machines, SVM. Both methods are trained using only defect free textured images for each type of analyzed texture, label-ing the samples for the SVMs in an automatic way. This work is based on two image processing streams using image sensors: (1) the CA first processes the incoming image from the input to the output, producing a reconstructed image, from which a measurement of correct or defective image is obtained; (2) the second process uses the latent layer information as input to the SVM to produce a measurement of classification. Both measurements are effectively combined, making an additional research contribution. The results obtained achieve a percentage of success of 92% on average, out-performing results of previous works.en
dc.identifier.citationTellaeche Iglesias, A., Campos Anaya, M. Á., Pajares Martinsanz, G., & Pastor‐lópez, I. (2021). On combining convolutional autoencoders and support vector machines for fault detection in industrial textures. Sensors, 21(10). https://doi.org/10.3390/S21103339
dc.identifier.doi10.3390/S21103339
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3340
dc.language.isoeng
dc.publisherMDPI AG
dc.rights© 2021 by the authors
dc.subject.otherAutoencoder
dc.subject.otherHybridization
dc.subject.otherImage sensors
dc.subject.otherSVM
dc.subject.otherTexture inspection
dc.titleOn combining convolutional autoencoders and support vector machines for fault detection in industrial texturesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue10
oaire.citation.titleSensors
oaire.citation.volume21
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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