Evaluation of Convolutional Neural Network Architectures for Detecting Drowsiness in Drivers

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dc.contributor.advisorAquino Cruz, Mario
dc.contributor.authorHurtado Delgado, Bryan
dc.contributor.authorOscco Guillen, Marycielo Xiomara
dc.date.accessioned2026-05-04T19:05:40Z
dc.date.available2026-05-04T19:05:40Z
dc.date.issued2025-09-09
dc.description.abstractDrowsiness in drivers is a condition that can manifest itself at any time, representing a constant challenge for road safety, especially in a context where artificial intelligence technologies are increasingly present in driver assistance systems. This paper presents a comparative evaluation of convolutional neural network (CNN) architectures for drowsiness detection, focusing on the identification of signals such as eye state and yawning. The research was of an applied type with a descriptive level, comparing the performance of LeNet, DenseNet121, InceptionV3 and MobileNet under challenging conditions, such as lighting and motion variations. A non-experimental design was used, with two datasets: a public dataset from Kaggle that included images classified into two categories (yawn and no yawn) and another created specifically for this study, which included images classified into three main categories (eyes open, eyes closed and undetected). The results indicated that, although all architectures performed well in controlled conditions, MobileNet stood out as the most accurate and consistent in challenging scenarios. DenseNet121 also showed good performance, while LeNet was effective in eye-state detection. This study provided a comprehensive assessment of the capabilities and limitations of CNNs for applications in drowsiness monitoring systems, and suggested future directions for improving accuracy in more challenging environments.
dc.formatapplication/pdf
dc.identifier.doihttps://dx.doi.org/10.14569/IJACSA.2025.0160217
dc.identifier.urihttps://hdl.handle.net/20.500.14195/1562
dc.language.isospa
dc.publisherUniversidad Nacional Micaela Bastidas de Apurímac
dc.publisher.countryPE
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectArchitectures
dc.subjectDetection
dc.subjectDrowsiness
dc.subjectNeural networks
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04
dc.titleEvaluation of Convolutional Neural Network Architectures for Detecting Drowsiness in Drivers
dc.typehttp://purl.org/coar/resource_type/c_7a1f
dc.type.versioninfo:eu-repo/semantics/publishedVersion
renati.advisor.dni41202588
renati.advisor.orcidhttps://orcid.org/0000-0002-2552-5669
renati.author.dni76188710
renati.author.dni75904811
renati.discipline61209206
renati.jurorLuque Ochoa, Evelyn Naida
renati.jurorCuentas Toledo, Maryluz
renati.jurorMamani Coaquira, Yonatan
renati.levelhttps://purl.org/pe-repo/renati/level#tituloProfesional
renati.typehttps://purl.org/pe-repo/renati/type#tesis
thesis.degree.disciplineIngeniería informática y sistemas
thesis.degree.grantorUniversidad Nacional Micaela Bastidas de Apurímac
thesis.degree.nameIngeniero Informático y Sistemas

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