Adaptive Driver Face Feature Fatigue Detection Algorithm Research

Fatigued driving is one of the leading causes of traffic accidents, and detecting fatigued driving effectively is critical to improving driving safety. Given the variety and individual variability of the driving surroundings, the drivers’ states of weariness, and the uncertainty of the key character...

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Bibliographic Details
Main Authors: Han Zheng, Yiding Wang, Xiaoming Liu
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/13/8/5074
Description
Summary:Fatigued driving is one of the leading causes of traffic accidents, and detecting fatigued driving effectively is critical to improving driving safety. Given the variety and individual variability of the driving surroundings, the drivers’ states of weariness, and the uncertainty of the key characteristic factors, in this paper, we propose a deep-learning-based study of the <i>MAX-MIN</i> driver fatigue detection algorithm. First, the ShuffleNet V2K16 neural network is used for driver face recognition, which eliminates the influence of poor environmental adaptability in fatigue detection; second, ShuffleNet V2K16 is combined with Dlib to obtain the coordinates of driver face feature points; and finally, the values of <i>EAR</i> and <i>MAR</i> are obtained by comparing the first 100 frames of images to <i>EAR-MAX</i> and <i>MAR-MIN</i>. Our proposed method achieves 98.8% precision, 90.2% recall, and 94.3% F-Score in the actual driving scenario application.
ISSN:2076-3417