A pre-Tranind Model for Driver Drowsiness Detection

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dc.contributor.authorAhmed, Amira
dc.date.accessioned2024-03-14T12:44:09Z
dc.date.available2024-03-14T12:44:09Z
dc.date.issued2023-06-22
dc.description.abstractDrowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness. Driver monitoring systems usually detect three types of information: biometric information, vehicle behavior, and the driver’s graphic information. Drowsiness detection methods based on the three types of information are discussed. A prospect for arousal level detection and estimation technology for autonomous driving is also presented. The technology will not be used to detect and estimate wakefulness for accident prevention; rather, it can be used to ensure that the driver has enough sleep to arrive comfortably at the destination. In this paper, we propose a Resnet (50) pre-trained model for driver drowsiness detection that achieves robust results and reaches 98% accuracy.
dc.identifier.doihttps://doi.org/10.31730/osf.io/w4ax8
dc.identifier.urihttps://africarxiv.ubuntunet.net/handle/1/478
dc.identifier.urihttps://doi.org/10.60763/africarxiv/436
dc.identifier.urihttps://doi.org/10.60763/africarxiv/436
dc.identifier.urihttps://doi.org/10.60763/africarxiv/436
dc.subjectDriver Detection
dc.subjectdeep learning
dc.subjectResNet(50)
dc.titleA pre-Tranind Model for Driver Drowsiness Detection

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