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dc.contributor.authorIntxausti Arbaiza, Eneko
dc.contributor.authorZugasti, Ekhi
dc.contributor.authorCernuda, Carlos
dc.contributor.otherLeibar, Ane Miren
dc.contributor.otherElizondo, Estibaliz
dc.date.accessioned2024-03-18T09:53:17Z
dc.date.available2024-03-18T09:53:17Z
dc.date.issued2023
dc.identifier.isbn978-3-031-49018-7en
dc.identifier.issn1611-3349en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=173969en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6291
dc.description.abstractDefect detection plays a vital role in ensuring product quality and safety within industrial casting processes. In these dynamic environments, the occasional emergence of new defects in the production line poses a significant challenge for supervised methods. We present a defect detection framework to effectively detect novel defect patterns without prior exposure during training. Our method is based on contrastive learning applied to the Faster R-CNN model, enhanced with a contrastive head to obtain discriminative representations of different defects. By training on an diverse and comprehensive labeled dataset, our method achieves comparable performance to the supervised baseline model, showcasing commendable defect detection capabilities. To evaluate the robustness of our approach, we authentically replicate a real-world use case by deliberately excluding several defect types from the training data. Remarkably, in this new context, our proposed method significantly improves detection performance of the baseline model, particularly in situations with very limited training data, showcasing a remarkable 34.7% enhancement. Our research highlights the potential of the proposed method in real-world environments where the number of available images may be limited or inexistent. By providing valuable insights into defect detection in challenging scenarios, our framework could contribute to ensuring efficient and reliable product quality and safety in industrial manufacturing processes.en
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherSpringeren
dc.rights© 2023 Springeren
dc.subjectDefect detectionen
dc.subjectcontrastive learningen
dc.subjectcastingen
dc.subjectoptical quality controlen
dc.subjectdeep learningen
dc.titleTowards robust defect detection in casting using contrastive learningen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.source26th Iberoamerican Congress on Pattern Recognition (CIARP 2023). Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Lecture Notes in Computer Scienceen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1007/978-3-031-49018-7_43en
local.embargo.enddate2025-11-30
local.contributor.otherinstitutionFagor Ederlan, S. Coop.es
local.contributor.otherinstitutionEdertek S. Coop.es
local.source.detailsVol. 14469. Pp. 605-616.en
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen
oaire.funderNameEusko Jaurlaritza = Gobierno Vasco
oaire.funderIdentifierhttps://ror.org/00pz2fp31 http://data.crossref.org/fundingdata/funder/10.13039/501100003086
oaire.fundingStreamElkartek 2022
oaire.awardNumberKK-2022/00049
oaire.awardTitleDeeplearning REcomendation Manufacturing Imperfection Novelty Detection (DREMIND)
oaire.awardURISin información


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