Visual-based Real Time Driver Drowsiness Detection System Using CNN

Flores-Monroy, Jonathan; Nakano-Miyatake, Mariko; Sánchez, Gabriel

doi:10.1109/cce53527.2021.9633082

Cited by 6 publications

(7 citation statements)

References 13 publications

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“…Una vez que se segmenta el rostro del conductor, la imagen resultante se inserta en una red neuronal profunda (Flores-Monroy et al, 2021), mostrada en la Fig. 6, la cual llevará a cabo el proceso de detección de somnolencia o distracción.…”

Section: Red Neuronal Profundaunclassified

“…Otro factor importante es el uso de teléfonos celulares durante la conducción lo que causa la falta de atención del conductor en los eventos circundantes. Considerando que la mayoría de los accidentes son causados por el conductor y cerca del 33% de estos se deben a somnolencia, cansancio y distracción del conductor, durante los últimos años se han propuesto diversos esquemas para detectar somnolencia y distracción del conductor a fin de evitar un lamentable accidente (Chacon, 2015;Flores-Monroy et al 2021;Uma y Eswari, 2021). Las técnicas usadas para detectar somnolencia en los conductores se pueden dividir básicamente en tres grupos: Métodos basados en comportamiento del vehículo, métodos basados en señales biológicas de conductor y el método basado en visión por computadora.…”

Section: Introductionunclassified

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Detección de somnolencia y distracción en conductores y su implementación en dispositivos móviles

Flores-Monroy,

Nakano-Miyatake,

Escamilla-Hernández

et al. 2023

Inf. tecnol.

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Este estudio propone un sistema que aplica técnicas de inteligencia artificial para reducir el número de accidentes vehiculares debido al cansancio o distracción del conductor. El sistema puede ser usado en cualquier marca o modelo de vehículo. Inicialmente detecta el rostro del conductor, el cual se introduce a una red neuronal profunda que lo analiza. La salida se inserta en una etapa de detección que determina si el conductor está distraído o somnoliento, activando una alarma. El sistema presenta una baja complejidad computacional, lo cual permite su realización en tiempo real en dispositivos móviles. Los resultados experimentales muestran que el sistema propuesto presenta una precisión de 95.8% al detectar somnolencia y distracción en un cómputo de alto rendimiento. También tiene un 84.5% de precisión en dispositivos móviles con capacidad limitada, manteniendo una velocidad de operación aceptable para la implementación en tiempo real. Se concluye que el sistema propuesto detecta distracción y somnolencia en el conductor con alta precisión.

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Section: Red Neuronal Profundaunclassified

Section: Introductionunclassified

Detección de somnolencia y distracción en conductores y su implementación en dispositivos móviles

Flores-Monroy,

Nakano-Miyatake,

Escamilla-Hernández

et al. 2023

Inf. tecnol.

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“…The proposed framework depends on PC vision and inserted framework applications. Eye conclusion is recognized utilizing HAAR based overflow classifier and a liquor gas sensor what capabilities as a Breathalyzer [4]. This framework incorporates two modules.…”

Section: Literature Overviewmentioning

confidence: 99%

A Novel Approach to Driver Drowsiness Detection System Using Neural Networks

2022

IRJMETS

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Sleepiness of the drivers is the primary driver of mishaps on the planet as well as Sri Lanka. Due to absence of rest and sluggishness, tiredness can happen while driving. The most effective way to stay away from mishaps brought about by drivers' sleepiness is to recognize tiredness of the driver and caution him before fall into rest. To recognize sleepiness numerous methods like eye retina location, facial component acknowledgment has been used. The need is on working on the wellbeing of the driver without being prominent. In this undertaking the eye flicker of the driver is distinguished. On the off chance that the drivers eyes stay shut for more than a certain timeframe, the driver is supposed to be tired and an alert is sounded. An endless number of individuals drive on the expressway constantly. Cabbies, transport drivers, transporters and individuals voyaging significant distance experience the ill effects of absence of rest. Because of which it turns out to be extremely perilous to drive while feeling sleepy. The greater part of mishaps occur because of the tiredness of the driver. Thus, to forestall these mishaps we will fabricate a framework utilizing Python, OpenCV, and Keras which will caution the driver when he feels sluggish.

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“…Although this approach provides better performance, it is invasive to the driver because the driver must use the wearable sensors during driving. Finally, in the visual-based approach, the driver's face image or video data are analyzed to determine the driver's drowsiness and distraction levels [11][12][13][14]. The principal advantage of this approach is that it is not invasive for drivers and the performance does not depend on the driver's driving skill, vehicle type, or the road conditions.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the face localization is carried out using a Haar-based face detector [15] or a MediaPipe face detector [16]. The drowsiness detection and/or distraction are carried out using some convolutional neural networks (CNN) [13,14], such as VGG16, MobileNet, ResNet, and some specifically designed CNNs for this purpose [11,12]. Almost all systems using this approach perform well in a laboratory environment using the GPU-based workstation, in which there are not any limitations in computing power, memory space, or energy consumption.…”

Section: Introductionmentioning

confidence: 99%

SOMN_IA: Portable and Universal Device for Real-Time Detection of Driver’s Drowsiness and Distraction Levels

et al. 2022

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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.

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Visual-based Real Time Driver Drowsiness Detection System Using CNN

Cited by 6 publications

References 13 publications

Detección de somnolencia y distracción en conductores y su implementación en dispositivos móviles

Detección de somnolencia y distracción en conductores y su implementación en dispositivos móviles

A Novel Approach to Driver Drowsiness Detection System Using Neural Networks

SOMN_IA: Portable and Universal Device for Real-Time Detection of Driver’s Drowsiness and Distraction Levels

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