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dc.contributor.authorSánchez Chica, Ander
dc.contributor.authorUgartemendia Telleria, Beñat
dc.contributor.authorZulueta Guerrero, Ekaitz ORCID
dc.contributor.authorFernández Gámiz, Unai
dc.contributor.authorGómez Hidalgo, Javier María
dc.date.accessioned2023-03-13T18:10:06Z
dc.date.available2023-03-13T18:10:06Z
dc.date.issued2023-03-01
dc.identifier.citationMathematics 11(5) : (2023) // Article ID 1205es_ES
dc.identifier.issn2227-7390
dc.identifier.urihttp://hdl.handle.net/10810/60344
dc.description.abstractRobots play a pivotal role in the manufacturing industry. This has led to the development of computer vision. Since AlexNet won ILSVRC, convolutional neural networks (CNNs) have achieved state-of-the-art status in this area. In this work, a novel method is proposed to simultaneously detect and predict the localization of objects using a custom loop method and a CNN, performing two of the most important tasks in computer vision with a single method. Two different loss functions are proposed to evaluate the method and compare the results. The obtained results show that the network is able to perform both tasks accurately, classifying images correctly and locating objects precisely. Regarding the loss functions, when the target classification values are computed, the network performs better in the localization task. Following this work, improvements are expected to be made in the localization task of networks by refining the training processes of the networks and loss functions.es_ES
dc.description.sponsorshipThe current study was sponsored by the Government of the Basque Country-ELKARTEK21/10 KK-2021/00014 (“Estudio de nuevas técnicas de inteligencia artificial basadas en Deep Learning dirigidas a la optimización de procesos industriales”) research program.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/es/
dc.subjectimage classificationes_ES
dc.subjectobject detectiones_ES
dc.subjectdeep learninges_ES
dc.subjectdeep convolutional neural networkses_ES
dc.subjectcomputer visiones_ES
dc.subjectcustom training loopes_ES
dc.titleA New Loss Function for Simultaneous Object Localization and Classificationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.date.updated2023-03-10T14:03:32Z
dc.rights.holder© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).es_ES
dc.relation.publisherversionhttps://www.mdpi.com/2227-7390/11/5/1205es_ES
dc.identifier.doi10.3390/math11051205
dc.departamentoesIngeniería Energética
dc.departamentoesIngeniería de sistemas y automática
dc.departamentoeuEnergia Ingenieritza
dc.departamentoeuSistemen ingeniaritza eta automatika


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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).
Except where otherwise noted, this item's license is described as © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).