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Please use this identifier to cite or link to this item: https://repository.esi-sba.dz/jspui/handle/123456789/706
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dc.contributor.authorCHACHOUA, AMani-
dc.contributor.authorDJABER, ROfaida-
dc.date.accessioned2024-10-03T09:29:34Z-
dc.date.available2024-10-03T09:29:34Z-
dc.date.issued2024-
dc.identifier.urihttps://repository.esi-sba.dz/jspui/handle/123456789/706-
dc.descriptionSupervisor : Pr. Sidi Mohammed BENSLIMANE Co-supervisor : Dr. Rabab BOUSMAHAen_US
dc.description.abstractThe status of the driver is critical, as a significant number of vehicular accidents are attributed to driver inattention or drowsiness. Implementing a drowsiness detection system in vehicles can greatly reduce the incidence of such accidents. Given that accidents can occur from just a momentary lapse in attention, it is imperative to have a driver monitoring system that operates in real-time. This detection system should be capable of being deployed on an embedded device while maintaining high accuracy. In this thesis, we introduce a novel approach to real-time drowsiness detection using deep learning techniques. Our approach is designed to be implemented on a low-cost embedded board, ensuring both cost-effectiveness and high performance. The primary contribution of this thesis is the creation of a real-time driver drowsiness detection system that leverages computer vision and artificial intelligence on an embedded device. This system aims to enhance road safety by continuously monitoring the driver’s state and providing timely alerts to prevent accidents caused by drowsiness. *** Le statut du conducteur est critique, car un nombre important d’accidents de la route sont attribu´es `a l’inattention ou `a la somnolence du conducteur. La mise en oeuvre d’un syst`eme de d´etection de somnolence dans les v´ehicules peut r´eduire consid´erablement l’incidence de tels accidents. ´Etant donn´e que des accidents peuvent survenir `a la suite d’un simple manque d’attention momentan´e, il est imp´eratif de disposer d’un syst`eme de surveillance du conducteur qui fonctionne en temps r´eel. Ce syst`eme de d´etection doit pouvoir ˆetre d´eploy´e sur un appareil embarqu´e tout en conservant une grande pr´ecision. Dans cette th`ese, nous introduisons une nouvelle approche de d´etection de la somnolence en temps r´eel `a l’aide de techniques d’apprentissage profond. Notre approche est con¸cue pour ˆetre mise en oeuvre sur une carte embarqu´ee `a faible coˆut, garantissant `a la fois rentabilit´e et hautes performances. La principale contribution de cette th`ese est la cr´eation d’un syst`eme de d´etection de la somnolence du conducteur en temps r´eel qui exploite la vision par ordinateur et l’intelligence artificielle sur un appareil embarqu´e. Ce syst`eme vise `a am´eliorer la s´ecurit´e routi`ere en surveillant en permanence l’´etat du conducteur et en fournissant des alertes opportunes pour pr´evenir les accidents caus´es par la somnolence.en_US
dc.language.isoenen_US
dc.subjectDriver Monitoring Systemen_US
dc.subjectDrowsiness Detectionen_US
dc.subjectDeep Learningen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectComputer Visionen_US
dc.subjectEmbedded Systemen_US
dc.titleReal-time Driver Drowsiness Detection System Using Deep Learningen_US
dc.typeThesisen_US
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