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Gaussian Weighted Eye State Determination for Driving Fatigue Detection.
- Source :
-
Mathematics (2227-7390) . May2023, Vol. 11 Issue 9, p2101. 24p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *EYESTRAIN
*INFRARED imaging
*TRAFFIC accidents
Subjects
Details
- Language :
- English
- ISSN :
- 22277390
- Volume :
- 11
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- Mathematics (2227-7390)
- Publication Type :
- Academic Journal
- Accession number :
- 163694361
- Full Text :
- https://doi.org/10.3390/math11092101