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Article

Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT

1
Department of Software, Anyang University, Anyang 14028, Korea
2
Liberal and Arts College, Anyang University, Anyang 14028, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(7), 3037; https://0-doi-org.brum.beds.ac.uk/10.3390/su12073037
Received: 26 February 2020 / Revised: 29 March 2020 / Accepted: 7 April 2020 / Published: 10 April 2020
(This article belongs to the Special Issue Big Data for Sustainable Anticipatory Computing)
In recent years, the casualties of traffic accidents caused by driving cars have been gradually increasing. In particular, there are more serious injuries and deaths than minor injuries, and the damage due to major accidents is increasing. In particular, heavy cargo trucks and high-speed bus accidents that occur during driving in the middle of the night have emerged as serious social problems. Therefore, in this study, a drowsiness prevention system was developed to prevent large-scale disasters caused by traffic accidents. In this study, machine learning was applied to predict drowsiness and improve drowsiness prediction using facial recognition technology and eye-blink recognition technology. Additionally, a CO2 sensor chip was used to detect additional drowsiness. Speech recognition technology can also be used to apply Speech to Text (STT), allowing a driver to request their desired music or make a call to avoid drowsiness while driving. View Full-Text
Keywords: drowsy; driving; prevention; detection; real-time flicker recognition method drowsy; driving; prevention; detection; real-time flicker recognition method
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MDPI and ACS Style

Jang, S.-W.; Ahn, B. Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT. Sustainability 2020, 12, 3037. https://0-doi-org.brum.beds.ac.uk/10.3390/su12073037

AMA Style

Jang S-W, Ahn B. Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT. Sustainability. 2020; 12(7):3037. https://0-doi-org.brum.beds.ac.uk/10.3390/su12073037

Chicago/Turabian Style

Jang, Seok-Woo, and Byeongtae Ahn. 2020. "Implementation of Detection System for Drowsy Driving Prevention Using Image Recognition and IoT" Sustainability 12, no. 7: 3037. https://0-doi-org.brum.beds.ac.uk/10.3390/su12073037

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