TY  - JOUR
PY  - 2023//
TI  - A deep-learning approach to driver drowsiness detection
JO  - Safety (Basel)
A1  - Ahmed, Mohammed Imran Basheer
A1  - Alabdulkarem, Halah
A1  - Alomair, Fatimah
A1  - Aldossary, Dana
A1  - Alahmari, Manar
A1  - Alhumaidan, Munira
A1  - Alrassan, Shoog
A1  - Rahman, Atta
A1  - Youldash, Mustafa
A1  - Zaman, Gohar
SP  - e65
EP  - e65
VL  - 9
IS  - 3
N2  - Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver's eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.<p />  <p>Language: en</p>
LA  - en
SN  - 2313-576X
UR  - http://dx.doi.org/10.3390/safety9030065
ID  - ref1
ER  -