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dc.contributor.authorLópez Montiel, Miguel-
dc.contributor.authorOrozco Rosas, Ulises-
dc.contributor.authorSánchez-Adame, Moises-
dc.contributor.authorPicos, Kenia-
dc.contributor.authorMontiel, Oscar-
dc.contributor.otherCETYS Universidades_ES
dc.contributor.otherInstituto Politécnico Nacional, CITEDI-IPNes_ES
dc.date.accessioned2021-07-28T20:17:48Z-
dc.date.available2021-07-28T20:17:48Z-
dc.date.created2021-05-29-
dc.date.issued2021-07-19-
dc.identifier.issn2169-3536-
dc.identifier.urihttps://repositorio.cetys.mx/handle/60000/1086-
dc.description.abstractTraffic Sign Detection (TSD) is a complex and fundamental task for developing autonomous vehicles; it is one of the most critical visual perception problems since failing in this task may cause accidents. This task is fundamental in decision-making and involves different internal conditions such as the internal processing system or external conditions such as weather, illumination, and complex backgrounds. At present, several works are focused on the development of algorithms based on deep learning; however, there is no information on a methodology based on descriptive statistical analysis with results from a solid experimental framework, which helps to make decisions to choose the appropriate algorithms and hardware. This work intends to cover that gap. We have implemented some combinations of deep learning models (MobileNet v1 and ResNet50 v1) in a combination of the Single Shot Multibox Detector (SSD) algorithm and the Feature Pyramid Network (FPN) component for TSD in a standardized dataset (LISA), and we have tested it on different hardware architectures (CPU, GPU, TPU, and Embedded System). We propose a methodology and the evaluation method to measure two types of performance. The results show that the use of TPU allows achieving a processing training time 16.3 times faster than GPU and better results in terms of precision detection for one combination.es_ES
dc.description.sponsorshipIEEEes_ES
dc.language.isoenes_ES
dc.relation.ispartofseries9;2021-
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 México*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/mx/*
dc.subjectTraffic sign detectiones_ES
dc.subjectDeep learninges_ES
dc.subjectHardware accelerationes_ES
dc.subjectComputer visiones_ES
dc.subjectAutonomous vehicleses_ES
dc.subjectEmbedded systemses_ES
dc.subjectDigital systemses_ES
dc.titleEvaluation method of deep learning-based embedded systems for traffic sign detectiones_ES
dc.title.alternativeIEEE Accesses_ES
dc.typeArticlees_ES
dc.description.urlhttps://doi.org/10.1109/ACCESS.2021.3097969es_ES
dc.format.page101217 - 101238es_ES
dc.identifier.indexacionJCRes_ES
dc.subject.sedeSistemases_ES
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