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

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 detectio...

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Bibliographic Details
Published in2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) pp. 237 - 242
Main Authors Jabbar, Rateb, Shinoy, Mohammed, Kharbeche, Mohamed, Al-Khalifa, Khalifa, Krichen, Moez, Barkaoui, Kamel
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2020
Subjects
Android
Automobiles
Cameras
Computational modeling
Convolutional Neural Networks
Driver Behaviour Monitoring System
Drowsiness Detection
Electroencephalography
Facial Landmarks
Machine learning
Real-Time Deep Learning
Vehicle crash testing
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Summary: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.
DOI:10.1109/ICIoT48696.2020.9089484