Volume 39 Issue 4
Aug.  2021
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Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(4): 26-34
HUANG Ling, HONG Peixin, WU Zerong, LIU Jianrong, HUANG Zixu, CUI Zuan. A Detection Method for Drivers' Fatigue States Based on Normalization of Epidemic Prevention[J]. Journal of Transport Information and Safety, 2021, 39(4): 26-34. doi: 10.3963/j.jssn.1674-4861.2021.04.004
Citation: HUANG Ling, HONG Peixin, WU Zerong, LIU Jianrong, HUANG Zixu, CUI Zuan. A Detection Method for Drivers' Fatigue States Based on Normalization of Epidemic Prevention[J]. Journal of Transport Information and Safety, 2021, 39(4): 26-34. doi: 10.3963/j.jssn.1674-4861.2021.04.004
shu

A Detection Method for Drivers' Fatigue States Based on Normalization of Epidemic Prevention

doi: 10.3963/j.jssn.1674-4861.2021.04.004

  • School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China

  • Received Date: 2021-06-02
    Abstract
  • The detection of fatigue driving is a research branch of traffic safety, and wearing masks in the COVID-19 situation poses a new challenge. Therefore, the driver's face is detected by the single-shot multi-box detector(SSD)model based on ResNet-10, and the MobileNet-V2 model is used to classify masks. The test set verifies that the classifier can reach an accuracy of 98.50%. The histogram of the oriented gradient(HOG)feature combined with the support vector machine(SVM)classifier is used to detect the driver's face without wearing a mask. In the subsequent processing, the cascade regress is used to locate the feature points and extract the fatigue indices in the time window. The second judgment is used to perform the text and sound warnings for the fatigue state, and the judgment thresholds are adjusted in the awaken state. The algorithm experimented on pre-collected videos and NTHU-DDD can achieve the accuracy of 92.65 and 86.09% at the overall speed of 18.42 fps, respectively. The proposed framework shows strong robustness against the variation of wearing glasses, facial posture, and illumination, considering the interference of mask and real-time performance.

     

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