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357785

Real-time Driver Drowsiness Detection Using Deep Neural Networks

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Last updated: 05 Jan 2025

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Abstract

This paper presents a driver drowsiness  detection for accident prevention which is based on the curvature 
of the eye. Our attempt is to develop a deep learning model that  can use the input from a camera in real time by extracting the eyes  to detect the drowsiness of the drivers.This paper helps to resolve  the problem of drowsiness detection with an accuracy of 96% for  test and 99% for validation

DOI

10.21608/iiis.2024.357785

Authors

First Name

Daniel

Last Name

Halim

MiddleName

-

Affiliation

Faculty of Engineering Cairo University, Cairo, Egypt

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Orcid

-

First Name

Mariam

Last Name

Hanafy

MiddleName

-

Affiliation

Faculty of Engineering Cairo University, Cairo, Egypt

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-

Orcid

-

First Name

Youssef

Last Name

Lotfy

MiddleName

-

Affiliation

Faculty of Engineering Cairo University, Cairo, Egypt

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-

Orcid

-

First Name

Mohanad

Last Name

Deif

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-

Affiliation

Department of Artificial intelligence , College of Information Technology, Misr University for Science & Technology (MUST), 6th of October City 12566 , Egypt

Email

mohanad.deif@must.edu.eg

City

-

Orcid

-

First Name

Rania

Last Name

Elgohary

MiddleName

-

Affiliation

Department of Artificial intelligence, College of Information Technology, Misr University for Science & Technology

Email

rania.elgohary@must.edu.eg

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-

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-

Volume

1

Article Issue

2

Related Issue

48104

Issue Date

2024-06-01

Receive Date

2024-06-02

Publish Date

2024-06-01

Online ISSN

2682-258X

Article File

IIIS3577851717189200.pdf

PDF . 644.2kB

Link

https://iiis.journals.ekb.eg/article_357785.html

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https://iiis.journals.ekb.eg/service?article_code=357785

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357,785

Publication Type

Journal

Publication Title

International Integrated Intelligent Systems

Publication Link

https://iiis.journals.ekb.eg/

MainTitle

Real-time Driver Drowsiness Detection Using Deep Neural Networks

Details

Type

Article

Created At

21 Dec 2024