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

Veuillez utiliser cette adresse pour citer ce document : https://di.univ-blida.dz/jspui/handle/123456789/25323

Titre:	Implémentation sur Raspberry pi de modèles de détection de la somnolence par deep learning
Auteur(s):	Benaissa, Zineb Lardjane, Rania
Mots-clés:	drowsiness, detection, tensorflow, raspberry pi, deep learning,dlib, driving.
Date de publication:	2023
Editeur:	blida 1
Résumé:	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.
Description:	4.621.1.1222/p86
URI/URL:	https://di.univ-blida.dz/jspui/handle/123456789/25323
Collection(s) :	Mémoires de Master

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Fichier	Description	Taille	Format
mémoire-finale.pdf		3,55 MB	Adobe PDF	Voir/Ouvrir

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