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المؤلفJabbar, Rateb
المؤلفShinoy, Mohammed
المؤلفKharbeche, Mohamed
المؤلفAl-Khalifa, Khalifa
المؤلفKrichen, Moez
المؤلفBarkaoui, Kamel
تاريخ الإتاحة2023-10-16T06:59:51Z
تاريخ النشر2020-02-01
اسم المنشور2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
المعرّفhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089484
الاقتباسJabbar, 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
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85085482136&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/48528
الملخصA 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 المشروعThis 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعAndroid
Convolutional Neural Networks
Driver Behaviour Monitoring System
Drowsiness Detection
Facial Landmarks
Real-Time Deep Learning
العنوانDriver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application
النوعConference Paper
الصفحات237-242
dc.accessType Open Access


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