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Article

Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network

1
Department Computer Science, FST, University Sultan Moulay Sliman, 23000 Beni Mellal, Morocco
2
Computer Systems Engineering Laboratory Cadi Ayyad University, Faculty of Sciences Semlalia, 40000 Marrakech, Morocco
*
Author to whom correspondence should be addressed.
Received: 6 January 2020 / Revised: 23 February 2020 / Accepted: 25 February 2020 / Published: 4 March 2020
(This article belongs to the Special Issue Image/Video Processing and Coding)
In recent years, the rise of car accident fatalities has grown significantly around the world. Hence, road security has become a global concern and a challenging problem that needs to be solved. The deaths caused by road accidents are still increasing and currently viewed as a significant general medical issue. The most recent developments have made in advancing knowledge and scientific capacities of vehicles, enabling them to see and examine street situations to counteract mishaps and secure travelers. Therefore, the analysis of driver’s behaviors on the road has become one of the leading research subjects in recent years, particularly drowsiness, as it grants the most elevated factor of mishaps and is the primary source of death on roads. This paper presents a way to analyze and anticipate driver drowsiness by applying a Recurrent Neural Network over a sequence frame driver’s face. We used a dataset to shape and approve our model and implemented repetitive neural network architecture multi-layer model-based 3D Convolutional Networks to detect driver drowsiness. After a training session, we obtained a promising accuracy that approaches a 92% acceptance rate, which made it possible to develop a real-time driver monitoring system to reduce road accidents. View Full-Text
Keywords: driver fatigue detection; drowsiness; recurrent neural networks driver fatigue detection; drowsiness; recurrent neural networks
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MDPI and ACS Style

Ed-Doughmi, Y.; Idrissi, N.; Hbali, Y. Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network. J. Imaging 2020, 6, 8. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6030008

AMA Style

Ed-Doughmi Y, Idrissi N, Hbali Y. Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network. Journal of Imaging. 2020; 6(3):8. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6030008

Chicago/Turabian Style

Ed-Doughmi, Younes; Idrissi, Najlae; Hbali, Youssef. 2020. "Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network" J. Imaging 6, no. 3: 8. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6030008

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