Publication: Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images
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2022-02
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MDPI AG
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Abstract
This work presents the development of an ADAS (advanced driving assistance system)
focused on driver drowsiness detection, whose objective is to alert drivers of their drowsy state
to avoid road traffic accidents. In a driving environment, it is necessary that fatigue detection is
performed in a non-intrusive way, and that the driver is not bothered with alarms when he or she is
not drowsy. Our approach to this open problem uses sequences of images that are 60 s long and are
recorded in such a way that the subject’s face is visible. To detect whether the driver shows symptoms
of drowsiness or not, two alternative solutions are developed, focusing on the minimization of false
positives. The first alternative uses a recurrent and convolutional neural network, while the second
one uses deep learning techniques to extract numeric features from images, which are introduced into
a fuzzy logic-based system afterwards. The accuracy obtained by both systems is similar: around 65%
accuracy over training data, and 60% accuracy on test data. However, the fuzzy logic-based system
stands out because it avoids raising false alarms and reaches a specificity (proportion of videos in
which the driver is not drowsy that are correctly classified) of 93%. Although the obtained results do
not achieve very satisfactory rates, the proposals presented in this work are promising and can be
considered a solid baseline for future works.
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Keywords
Adas, Drowsiness, Deep learning, Convolutional neural networks, Recurrent neural networks, Fuzzy logic, Computer vision
Bibliographic citation
Magán, E., Sesmero, M. P., Alonso-Weber, J. M., & Sanchis, A. (2022). Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images. In Applied Sciences, 12(3), 1145-1170