Automated Surface Defect Detection in Metals: A Comparative Review of Object Detection and Semantic Segmentation Using Deep Learning

dc.contributor.author	Usamentiaga Fernández, Rubén
dc.contributor.author	González Lema, Darío
dc.contributor.author	Díaz Pedrayes, Óscar
dc.contributor.author	García Martínez, Daniel Fernando
dc.date.accessioned	2024-04-23T08:23:10Z
dc.date.available	2024-04-23T08:23:10Z
dc.date.issued	2022
dc.identifier.citation	IEEE Transactions on Industry Applications, 58(3), p. 4203 - 4213 (2022); doi:10.1109/TIA.2022.3151560
dc.identifier.issn	0093-9994
dc.identifier.uri	https://hdl.handle.net/10651/72387
dc.description.sponsorship	This work was supported by the Project RTI2018-094849-B-I00 of the Spanish National Plan for Research, Development and Innovation.	spa
dc.format.extent	p. 4203 - 4213	spa
dc.language.iso	eng	spa
dc.relation.ispartof	IEEE Transactions on Industry Applications, 58(3)	spa
dc.rights	© 2022 IEEE.
dc.title	Automated Surface Defect Detection in Metals: A Comparative Review of Object Detection and Semantic Segmentation Using Deep Learning	spa
dc.type	journal article	spa
dc.identifier.doi	10.1109/TIA.2022.3151560
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094849-B-I00/ES/OPTIMIZACION DE COMPONENTES EN ARQUITECTURAS PARA EL PROCESAMIENTO DE DATOS DE SENSORES EN EL CLOUD/	spa
dc.relation.publisherversion	http://dx.doi.org/10.1109/TIA.2022.3151560	spa

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