| Autor: |
Flores-Monroy, Jonathan, Nakano-Miyatake, Mariko, Escamilla-Hernandez, Enrique, Sanchez-Perez, Gabriel, Perez-Meana, Hector |
| Předmět: |
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| Zdroj: |
Electronics (2079-9292); Aug2022, Vol. 11 Issue 16, p2558-N.PAG, 27p |
| Abstrakt: |
In this paper, we propose a portable device named SOMN_IA, to detect drowsiness and distraction in drivers. The SOMN_IA can be installed inside of any type of vehicle, and it operates in real time, alerting the dangerous state caused by drowsiness and/or distraction in the driver. The SOMN_IA contains three types of alarm: light alarm, sound alarm, and the transmission of information about the driver's dangerous state to a third party if the driver does not correct his/her dangerous state. The SOMN_IA contains a face detector and a classifier based on the convolutional neural networks (CNN), and it aids in the management of consecutive information, including isolated error correction mechanisms. All of the algorithmic parts of the SOMN_IA are analyzed and adjusted to operate in real-time in a portable device with limited computational power and memory space. The SOMN_IA requires only a buck-type converter to connect to the car battery. The SONM_IA discriminates correctly between real drowsiness and normal blinking, as well as between real dangerous distraction and a driver's normal attention to his/her right and left. Although the real performance of the SOMN_IA is superior to the CNN classification accuracy thanks to isolated error correction, we compare the CNN classification accuracy with the previous systems. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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