Gaussian Weighted Eye State Determination for Driving Fatigue Detection

Yunjie Xiang, Rong Hu (), Yong Xu, Chih-Yu Hsu and Congliu Du
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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
Congliu Du: State Grid Tibet Electric Power Research Institute, Lhasa 850000, China

Mathematics, 2023, vol. 11, issue 9, 1-24

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.

Keywords: fatigue driving; eye-based fatigue state dataset; fatigue degree; face detection; image segmentation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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