Adaptive Driver Face Feature Fatigue Detection Algorithm Research
Source abstract
Abstract
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 MAX-MIN 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 EAR and MAR are obtained by comparing the first 100 frames of images to EAR-MAX and MAR-MIN. Our proposed method achieves 98.8% precision, 90.2% recall, and 94.3% F-Score in the actual driving scenario application.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sleep and Work-Related Fatigue · Ergonomics and Musculoskeletal Disorders
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
School of Artificial Intelligence and Manufacturing, Hechi University, Hechi 546300, China
College of Information Science and Technology, North China University of Technology, Beijing 100144, China
Funding
National Natural Science Foundation of China · the National Key Research and Development Program of China · Guangxi First-class Discipline Construction Project Electronic Information (Hechi University) · Key Laboratory of AI and Information Processing (Hechi University), Education Department of Guangxi Zhuang Autonomous Region
References (17)
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