Gaussian Weighted Eye State Determination for Driving Fatigue Detection

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+ netwo...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-04, Vol.11 (9), p.2101
Main Authors: Xiang, Yunjie, Hu, Rong, Xu, Yong, Hsu, Chih-Yu, Du, Congliu
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computer architecture
Datasets
Driver fatigue
eye-based fatigue state dataset
face detection
Fatigue
fatigue degree
fatigue driving
Food science
Frames per second
Image segmentation
Infrared imagery
Methods
Natural lighting
Physiology
Questionnaires
Semantics
Support vector machines
Traffic accidents
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11092101