| Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01042 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/e3sconf/202343001042 | |
| Published online | 06 October 2023 | |
Automated Detection of Drowsiness using Machine Learning Approach
1 Department of CSE (AI & ML), GRIET, Hyderabad, Telangana State, India
2 Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, 248007, India
3 KG Reddy College of Engineering & Technology, Hyderabad, India
* Corresponding author: sanjeeva1690@grietcollege.com
Nowadays, there is a steady rise in the number of traffic accidents. The primary causes of these accidents are impaired driving due to alcohol consumption and driver fatigue. The primary goal is to create a system capable of measuring a driver’s degree of sleepiness. If drowsiness is identified, a warning will be sent out via integration with an alert warning system and text message system. Drowsiness detection is built using OpenCV, Python, and Machine Learning. A significant number of annotated driver images depicting different levels of drowsiness, alongside images of diverse driving scenarios and lighting conditions, were utilized in the research to enhance the dataset. The system for detecting driver drowsiness provides a viable method to avert car accidents resulting from driver tiredness. It examines the driver’s eye and alerts them when necessary. Further improvements could increase the alarm system’s accuracy by minimizing the number of false warnings.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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