Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application
المؤلف | Jabbar, Rateb |
المؤلف | Shinoy, Mohammed |
المؤلف | Kharbeche, Mohamed |
المؤلف | Al-Khalifa, Khalifa |
المؤلف | Krichen, Moez |
المؤلف | Barkaoui, Kamel |
تاريخ الإتاحة | 2023-10-16T06:59:51Z |
تاريخ النشر | 2020-02-01 |
اسم المنشور | 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020 |
المعرّف | http://dx.doi.org/10.1109/ICIoT48696.2020.9089484 |
الاقتباس | Jabbar, R., Shinoy, M., Kharbeche, M., Al-Khalifa, K., Krichen, M., & Barkaoui, K. (2020, February). Driver drowsiness detection model using convolutional neural networks techniques for android application. In 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) (pp. 237-242). IEEE. |
الترقيم الدولي الموحد للكتاب | 9781728148212 |
الملخص | 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. |
راعي المشروع | This publication was made possible by an NPRP award [NPRP8-910-2-387] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | Android Convolutional Neural Networks Driver Behaviour Monitoring System Drowsiness Detection Facial Landmarks Real-Time Deep Learning |
النوع | Conference Paper |
الصفحات | 237-242 |
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