Driver Drowsiness Detection and Alert Systems

Drowsiness has significant contribution to the accidents on road. Accurate measurement is required to track the state of the driver. It has various shortcomings. Convolutional neural networks(CNN) developed using Keras were utilized to create the model that we employed. CNN is a branch of deep neura...

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Bibliographic Details
Published in:International journal for research in applied science and engineering technology 2023-05, Vol.11 (5), p.1417-1420
Main Authors: Ganesh, Supreetha, B, Amit K, Delvi, Akif, Shreyas, C N, K, Gagandeep
Format: Article
Language:English
Online Access:Get full text
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Summary:Drowsiness has significant contribution to the accidents on road. Accurate measurement is required to track the state of the driver. It has various shortcomings. Convolutional neural networks(CNN) developed using Keras were utilized to create the model that we employed. CNN is a branch of deep neural networks that is appropriate for image classification. It consists of many layers that include input, output and hidden layers. The drowsiness detection systems for drivers have the potential to greatly increase traffic safety by warning drivers to stop or take breaks when they are in danger of nodding off behind the wheel.
ISSN:2321-9653
2321-9653
DOI:10.22214/ijraset.2023.51768