Implémentation sur Raspberry pi de modèles de détection de la  somnolence par deep learning

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DC Field	Value	Language
dc.contributor.author	Benaissa, Zineb	-
dc.contributor.author	Lardjane, Rania	-
dc.date.accessioned	2023-10-05T10:39:10Z	-
dc.date.available	2023-10-05T10:39:10Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://di.univ-blida.dz/jspui/handle/123456789/25323	-
dc.description	4.621.1.1222/p86	fr_FR
dc.description.abstract	Our project aims to implement a drowsiness detection system based on deep learning on Raspberry Pi(Tensorflow). This combination offers a portable and energy-efficient solution for accurate and real-time detection. The study examines the hardware and software aspects specific to Raspberry Pi, as well as the system's performance compared to another drowsiness detection method (Dlib). The goal is to enhance safety and vigilance in domains such as automotive driving and surveillance.	fr_FR
dc.language.iso	fr	fr_FR
dc.publisher	blida 1	fr_FR
dc.subject	drowsiness, detection, tensorflow, raspberry pi, deep learning,dlib, driving.	fr_FR
dc.title	Implémentation sur Raspberry pi de modèles de détection de la somnolence par deep learning	fr_FR
dc.type	Other	fr_FR
Appears in Collections:	Mémoires de Master

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mémoire-finale.pdf		3,55 MB	Adobe PDF	View/Open

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