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|Title:||Driver drowsiness detection by employing CNN and DLIB||Authors:||Ali N.
Roads and streets
|Issue Date:||2021||Publisher:||Institute of Electrical and Electronics Engineers Inc.||Source:||Ali, N., Hasan, I., Özyer, T., & Alhajj, R. (2021, December). Driver Drowsiness Detection by Employing CNN and Dlib. In 2021 22nd International Arab Conference on Information Technology (ACIT) (pp. 1-5). IEEE.||Abstract:||Every year thousands of people lose their life due to road accidents. One of the main reasons for these accidents is driver drowsiness. In driver drowsiness, the driver slept while driving, which causes the road accident, especially on the long routes. Driver fatigue and micro sleep while driving caused the fatal accident and death of human beings. To overcome this problem, we are implementing a technique in which we capture the image of the driver. After capturing the image of the driver, we process driver images to detect driver drowsiness. For the processing of the driver image, we are using two different techniques with each other. In the first technique, we are using the Dlib for image drowsiness detection by detecting that driver’s eyes are closed and the driver is yawning. In the second technique, we used CNN for the detection of yawning and the eyes of the driver are closed or not and predict driver drowsiness. After implementing the two techniques we combine the output of both techniques. After combining both techniques we test the system, and it gives us very good results. © 2021 IEEE.||Description:||22nd International Arab Conference on Information Technology, ACIT 2021 -- 21 December 2021 through 23 December 2021 -- -- 176492||URI:||https://doi.org/10.1109/ACIT53391.2021.9677197
|Appears in Collections:||Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering|
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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checked on Dec 26, 2022
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