Detection of Driver Drowsiness using Eye Blink Sensor

B.M, Kusuma Kumari; Sethi, Sampada; P, Ramakanth Kumar; Kumar, Nitish; Shankar, Atulit

doi:10.14419/ijet.v7i3.12.16167

Cited by 14 publications

(4 citation statements)

References 9 publications

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“…Along with this, the owner will be informed. By this, the driver and the owner will be notified and can avoid further consequences [23]. To recognize the mental attention level of the driver the system makes use of a brain-computer interface along with two sensors.…”

Section: Literature Reviewmentioning

confidence: 99%

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

Vishesh¹,

Raghavendra²,

Jankatti³

et al. 2021

IJEECS

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<span><span>Blinking is a regular bodily function and it is the semiautomatic fast closing of the eyelid. A specific blink is examined by dynamic folding of the eyelid. It is a vital function of the eye which helps in spread of tears across and eliminates irritants from the shallow of cornea. In this research work we made use of convolution neural network, the deep learning concepts and image processing to detect drowsiness level in drivers. To train the blink detection model the mobilenet V2 is used as base. The loss function used for training was RMSprop and the optimizer is binary cross entropy. The dlib facial landmark was exploited to perceive and pre-process the detected faces. The dataset used for the training model is selected from the “Xiaoyang Tan” of nanjing university of aeronautics and astronautics. Based on the experimental outcome the projected method achieves an accuracy of 97%. The prototype developed serves as a base for further development of this process to achieve better road safety</span>.</span>

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Section: Literature Reviewmentioning

confidence: 99%

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

Vishesh¹,

Raghavendra²,

Jankatti³

et al. 2021

IJEECS

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show abstract

“…Truck drivers who drive for continuous long hours (especially at night), bus drivers of long-distance routes or overnight buses and those people who take their families to long destinations are more susceptible to this problem. Driver drowsiness [1] [2] is an overcast nightmare for passengers in every country whether it's a developing /developed country (America/Canada) or an underdeveloped country. The US National Highway Traffic Safety Administration has estimated that worldwide every year, about 100 000 road accidents are caused by drowsiness, accounting for > 1500 deaths and > 70,000 injuries.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Drowsy Driving Accident Prevention System

2023

IRJMETS

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To prevent accidents caused by drowsiness, we can use a device called an eye blink sensor that uses infrared (IR) rays. This sensor consists of two parts: an IR transmitter and an IR receiver. The transmitter emits IR rays towards the driver's eye. when the eye is closed, the sensor outputs a high signal, and when the eye is open, the output is low. This output is connected to an alarm system both inside and outside the vehicle. Inside the vehicle, an alarm will sound if the driver's eyes remain closed for too long, alerting them to the potential danger. The alarm will continue until the driver regains consciousness and takes control of the vehicle. If the driver is still unable to respond after a certain period of time, the alarm outside the vehicle will activate to alert others nearby and encourage them to assist the driver. In addition, this eye blink sensor can also be linked to the vehicle's braking system, allowing it to automatically reduce the vehicle's speed when drowsiness is detected, further enhancing safety measures.

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“…It is always vital to keep track of the driver's state of attentiveness to stay out of mishaps. [2].The motorist's lack of attention during driving is frequently identified as the primary root cause of devastating crashes on roads. And the frequency of mishaps can be significantly minimized by preventing drowsiness and notifying motorists.…”

Section: Introductionmentioning

confidence: 99%

A Review on Drowsy Driving Detection Device Using Arduino and Eye-Blink Sensor

Singh,

Pratap Singh,

Pal Singh

et al. 2023

IJEAST

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Driver inattention is one of the biggest contributors of road accidents. Driver fatigue and distraction are the two leading reasons of crashes involving vehicles. Tired drivers are just as dangerous as drunk drivers since they have shorter reaction times and less attention, awareness, and control of their automobiles. Driving while disoriented or distracted can result in a horrific death and can also endanger or jeopardize the safety of others. A fraction of a second of poor visual judgement can result in permanent blindness. Effective actions should be implemented to prevent driver drowsiness to lessen the frequency of road accidents. This paper has a significant impact on those who do not care for themselves. This is a safety mechanism that informs the driver if he becomes drowsy. The driver's eye movement is tracked, and if the driver falls asleep or closes their eyes for more than 2 seconds, a buzzer or vibrator will detect and notify the driver. Using an Arduino micro-controller as a foundation, this operation provides a low-cost option in automotive safety technology for accident avoidance and, as a result, reduces road traffic accidents. This paper aids in the prevention of such accidents and serves as a preventive measure to lower the danger of national accidents, particularly those caused by drowsy driving.

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Detection of Driver Drowsiness using Eye Blink Sensor

Cited by 14 publications

References 9 publications

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

Eye blink detection using CNN to detect drowsiness level in drivers for road safety

An Intelligent Drowsy Driving Accident Prevention System

A Review on Drowsy Driving Detection Device Using Arduino and Eye-Blink Sensor

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