Driver Drowsiness Detection using Hybrid Algorithm

Authors

U. Poorna Lakshmi
Vignana Bharathi Institute of Technology, Hyderabad
P.V.S. Srinivas
Vignana Bharathi Institute of Technology, Hyderabad
S Shyam
Department of Electronics and Communication Engineering, Vignan Bharathi Institute of Technology, Hyderabad
Mallikarjun Reddy Muchintala
Vignana Bharathi Institute of Technology, Hyderabad
Viswanath Reddy Palugulla
Vignana Bharathi Institute of Technology, Hyderabad
Hemanth Yadav Mandra
Vignana Bharathi Institute of Technology, Hyderabad
DOI: https://doi.org/10.52549/ijeei.v12i1.4738
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Published: 14-03-2024 • Issue: Vol. 12 No. 1: March 2024 • Section: Regular paper

Abstract

In this work we focus on the discernment of sleepiness in drivers’ drowsiness proposing a hybrid algorithm which aims to confirm whether the driver's level of attention has decreased owing to a nap or any other medical issue, such as brain problems. Therefore, the proposed hybrid algorithm uses both Haarcascade classifier and Convolutional Neural Network (CNN) algorithm to detect drivers’ drowsiness. The driver's eyes will be monitored and an alert sound will be generated by Raspberry Pi module, but the face must be moving in real time, and the aspect ratio must be between 16:9 and 1.85:1. People often feel sleepy since activities like driving call for a proper mental state, and bad work-life balance has additional negative repercussions. When we give input through normal camera it analyses drivers state of eyes and mouth, actually it checks aspect ratio of eye. We proved in comparative trials that our hybrid algorithm beats current driving fatigue detection algorithms in speed as well as accuracy.
Keywords: Convolutional Neural Network (CNN); Drowsiness Detection; Haar cascade Classifier; OpenCV

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Author Biographies

U. Poorna Lakshmi
Department of ECE, Professor
P.V.S. Srinivas
Professor, Department of Computer Science and Engineering
Mallikarjun Reddy Muchintala
Department of Electronics and Communication Engineering
Viswanath Reddy Palugulla
Department of Electronics and Communication Engineering
Hemanth Yadav Mandra
Department of Electronics and Communication Engineering

How to Cite

[1]
U. P. Lakshmi, P. Srinivas, S. Shyam, M. R. Muchintala, V. R. Palugulla, and H. Y. Mandra, “Driver Drowsiness Detection using Hybrid Algorithm”, IJEEI, vol. 12, no. 1, pp. 171–178, Mar. 2024, doi: 10.52549/ijeei.v12i1.4738.