Université Blida 1
Implémentation sur Raspberry pi de modèles de détection de la somnolence par deep learning
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| 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 |
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