Modern Drowsiness Detection in Deep Learning: A review

  • Sarah Saadoon Jasim Department of IT, Technical college of Management-Baghdad, Middle Technical University, Baghdad, Iraq
  • Alia Karim Abdul Hassan Department of Computer Science, University of Technology, Baghdad, Iraq
Keywords: driver drowsiness, deep learning, CNN, behavioral techniques, physiological features


Recent statistics show that drowsiness is now more of a factor in some car accidents than alcohol. So numerous monitoring systems have been developed to reduce, and accidents eliminate these events. Drowsiness detection systems are plentiful, but it isn't clear which one is the most effective. This review paper will address the following issues: firstly, determining the extracted features, whether behavioral-based, physiological-based, or vehicle-based, which are used to detect driver drowsiness; secondly, focusing on using deep learning to detect driver drowsiness. Finally, concluding from this study that the hybrid-based features should be used Because it gives strength in determining the drowsiness of the driver.


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How to Cite
Jasim, S., & Abdul Hassan, A. (2022). Modern Drowsiness Detection in Deep Learning: A review. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(3), Comp Page 119-129.
Computer article