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

Seok-Woo Jang and Byeongtae Ahn
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Seok-Woo Jang: Department of Software, Anyang University, Anyang 14028, Korea
Byeongtae Ahn: Liberal and Arts College, Anyang University, Anyang 14028, Korea

Sustainability, 2020, vol. 12, issue 7, 1-13

Abstract: 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 CO 2 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.

Keywords: drowsy; driving; prevention; detection; real-time flicker recognition method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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