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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Real-time Driver Drowsiness Detection using Deep Learning
Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0120794
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 7, 2021.
Abstract: Every year thousands of lives pass away worldwide due to vehicle accidents, and the main reason behind this is the drowsiness in drivers. A drowsiness detection system will help to reduce this accident and save many lives around the world. To defend this problem, we propose a methodology based on Convolutional Neural Networks (CNN) that illustrates drowsiness detection as a task to detect an object. It will detect and localize whether the eyes are open or close based on the real-time video stream of drivers. The MobileNet CNN Architecture with Single Shot Multibox Detector is the technology used for this object detection task. A separate algorithm is used based on the output given by the SSD_MobileNet_v1 architecture. A dataset that consists of around 4500 images was labeled with the object’s face yawn, no-yawn, open eye, and closed eye to train the SSD_MobileNet_v1 Network. Around 600 randomly selected images are used to test the trained model using the PASCAL VOC metric. The proposed approach is to ensure better accuracy and computational efficiency. It is also affordable as it can process incoming video streams in real-time and does not need any expensive hardware support. There only needs a standalone camera to be implemented using cheap devices in cars using Raspberry Pi 3 or other IP cameras.
Keywords: Deep learning; drowsiness detection; object detection; MobileNets; Single Shot Multibox Detector
Md. Tanvir Ahammed Dipu, Syeda Sumbul Hossain, Yeasir Arafat and Fatama Binta Rafiq, “Real-time Driver Drowsiness Detection using Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 12(7), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120794
@article{Dipu2021,
title = {Real-time Driver Drowsiness Detection using Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120794},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120794},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {7},
author = {Md. Tanvir Ahammed Dipu and Syeda Sumbul Hossain and Yeasir Arafat and Fatama Binta Rafiq}
}
