Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ International Journa...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
International Journal of Engineering Research and
Article . 2020 . Peer-reviewed
Data sources: Crossref
ZENODO
Article . 2020
License: CC BY NC
Data sources: Datacite
ZENODO
Article . 2020
License: CC BY NC
Data sources: Datacite
versions View all 3 versions
addClaim

Driver Drowsiness Detection using Microservices and Convolutional Neural Network

Authors: Shrut Shah;

Driver Drowsiness Detection using Microservices and Convolutional Neural Network

Abstract

Road accidents are one of the main contributors to net fatality rates in India. According to a recent survey in 2020, 43% of road accidents come from drowsy driving. Driving over hours and being in the same state makes the driver feel exhausted and fatigue leading them to drowsiness. A report from Road Transport of India stated that on average 5210 tragedies occur each year alone on the highways of India. A primary system to measure and alert the driver must be mandatory for any moving vehicle. In this paper, a modern approach is proposed for real-time drowsiness detection. A production-grade application with microservice architecture is one of the main focus of this paper. The process of building up the data, augmenting it to a desired level and finally labeling is presented. The customized state of art model is proposed that can achieve an accuracy of 83.65%.

Keywords

Deeplearning; microservices; drowsiness detection system; real-time application; kubernetes

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
hybrid