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Gaussian Weighted Eye State Determination for Driving Fatigue Detection

Author

Listed:
  • Yunjie Xiang

    (Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350118, China)

  • Rong Hu

    (Fujian Provincial Key Laboratory of Big Data Mining and Applications, School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China)

  • Yong Xu

    (Fujian Provincial Key Laboratory of Big Data Mining and Applications, School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China)

  • Chih-Yu Hsu

    (School of Transportation, Fujian University of Technology, Fuzhou 350118, China
    Intelligent Transportation System Research Center, Fujian University of Technology, Fuzhou 350118, China)

  • Congliu Du

    (State Grid Tibet Electric Power Research Institute, Lhasa 850000, China)

Abstract

Fatigue is a significant cause of traffic accidents. Developing a method for determining driver fatigue level by the state of the driver’s eye is a problem that requires a solution, especially when the driver is wearing a mask. Based on previous work, this paper proposes an improved DeepLabv3+ network architecture (IDLN) to detect eye segmentation. A Gaussian-weighted Eye State Fatigue Determination method (GESFD) was designed based on eye pixel distribution. An EFSD (Eye-based Fatigue State Dataset) was constructed to verify the effectiveness of this algorithm. The experimental results showed that the method can detect a fatigue state at 33.5 frames-per-second (FPS), with an accuracy of 94.4%. When this method is compared to other state-of-the-art methods using the YawDD dataset, the accuracy rate is improved from 93% to 97.5%. We also performed separate validations on natural light and infrared face image datasets; these validations revealed the superior performance of our method during both day and night conditions.

Suggested Citation

  • Yunjie Xiang & Rong Hu & Yong Xu & Chih-Yu Hsu & Congliu Du, 2023. "Gaussian Weighted Eye State Determination for Driving Fatigue Detection," Mathematics, MDPI, vol. 11(9), pages 1-24, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2101-:d:1135687
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    References listed on IDEAS

    as
    1. Chih-Yu Hsu & Rong Hu & Yunjie Xiang & Xionghui Long & Zuoyong Li, 2022. "Improving the Deeplabv3+ Model with Attention Mechanisms Applied to Eye Detection and Segmentation," Mathematics, MDPI, vol. 10(15), pages 1-20, July.
    2. Zhou, Zhuping & Cai, Yifei & Ke, Ruimin & Yang, Jiwei, 2017. "A collision avoidance model for two-pedestrian groups: Considering random avoidance patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 142-154.
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