TY - JOUR
T1 - SOMN_IA
T2 - Portable and Universal Device for Real-Time Detection of Driver’s Drowsiness and Distraction Levels
AU - Flores-Monroy, Jonathan
AU - Nakano-Miyatake, Mariko
AU - Escamilla-Hernandez, Enrique
AU - Sanchez-Perez, Gabriel
AU - Perez-Meana, Hector
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/8
Y1 - 2022/8
N2 - 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.
AB - 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.
KW - convolutional neural networks (CNN)
KW - driver’s distraction level
KW - driver’s drowsiness level
KW - face detection
KW - hardware implementation
KW - portable device
KW - real-time operation
UR - http://www.scopus.com/inward/record.url?scp=85137400167&partnerID=8YFLogxK
U2 - 10.3390/electronics11162558
DO - 10.3390/electronics11162558
M3 - Artículo
AN - SCOPUS:85137400167
SN - 2079-9292
VL - 11
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 16
M1 - 2558
ER -