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Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application

Abstract : A sleepy driver is arguably much more dangerous on the road than the one who is speeding as he is a victim of microsleeps. Automotive researchers and manufacturers are trying to curb this problem with several technological solutions that will avert such a crisis. This article focuses on the detection of such micro sleep and drowsiness using neural network-based methodologies. Our previous work in this field involved using machine learning with multi-layer perceptron to detect the same. In this paper, accuracy was increased by utilizing facial landmarks which are detected by the camera and that is passed to a Convolutional Neural Network (CNN) to classify drowsiness. The achievement with this work is the capability to provide a lightweight alternative to heavier classification models with more than 88% for the category without glasses, more than 85% for the category night without glasses. On average, more than 83% of accuracy was achieved in all categories. Moreover, as for model size, complexity and storage, there is a marked reduction in the new proposed model in comparison to the benchmark model where the maximum size is 75 KB. The proposed CNN based model can be used to build a real-time driver drowsiness detection system for embedded systems and Android devices with high accuracy and ease of use.
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Contributor : Kamel BARKAOUI Connect in order to contact the contributor
Submitted on : Sunday, October 31, 2021 - 2:48:59 PM
Last modification on : Wednesday, September 28, 2022 - 5:57:53 AM
Long-term archiving on: : Tuesday, February 1, 2022 - 6:02:14 PM


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Rateb Jabbar, Mohammed Shinoy, Mohamed Kharbeche, Khalifa Al-Khalifa, Moez Krichen, et al.. Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application. ICIoT 2020, 2020, Doha, Qatar. pp.237-242, ⟨10.1109/ICIoT48696.2020.9089484⟩. ⟨hal-02479367⟩



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