Adaptive Driver Face Feature Fatigue Detection Algorithm Research
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Published:2023-04-18
Issue:8
Volume:13
Page:5074
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Zheng Han12ORCID, Wang Yiding3, Liu Xiaoming1ORCID
Affiliation:
1. College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China 2. School of Artificial Intelligence and Manufacturing, Hechi University, Hechi 546300, China 3. College of Information Science and Technology, North China University of Technology, Beijing 100144, China
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.
Funder
National Natural Science Foundation of China the National Key Research and Development Program of China Guangxi First-class Discipline Construction Project Electronic Information Key Laboratory of AI and Information Processing (Hechi University), Education Department of Guangxi Zhuang Autonomous Region
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference17 articles.
1. A critical review of the psychophysiology of driver fatigue—ScienceDirect;Lal;Biol. Psychol.,2001 2. He, J., Chen, J., Liu, J., and Li, H. (2019, January 6–8). A Lightweight Architecture For Driver Status Monitoring Via Convolutional Neural Networks. Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China. 3. Abtahi, S., Omidyeganeh, M., Shirmohammadi, S., and Hariri, B. (2014, January 20–24). YawDD: A yawning detection dataset. Proceedings of the 5th ACM Multimedia Systems Conference, Virtual Event, China. 4. Guo, X., Li, S., Yu, J., Zhang, J., Ma, J., Ma, L., Liu, W., and Ling, H. (2019). PFLD: A practical facial landmark detector. arXiv. 5. Kreiss, S., Bertoni, L., and Alahi, A. (2019, January 15–20). Pifpaf: Composite fields for human pose estimation. Proceedings of the IEEE/CVF Conferaence on Computer Vision and Pattern Recognition, Long Beach, CA, USA.
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