Fatigue Detection Using Raspberry Pi 3

Chellappa, Akalya; Reddy, Mandi Sushmanth; Ezhilarasie, R.; Suguna, S.; Umamakeswari, A.

doi:10.14419/ijet.v7i2.24.11993

Cited by 24 publications

(6 citation statements)

References 7 publications

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“…Chellappa et al [8] suggest a system which was built for four-wheelers, and it detects and notifies the driver's tiredness or drowsiness. The suggested solution would employ a 5-megapixel Raspbian camera to record and evaluate photos of the driver's face and eyes in order to detect driver drowsiness.…”

Section: Literature Reviewmentioning

confidence: 99%

Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning

Majeed,

Hussein Ali,

A. Al-Hilali

et al. 2023

Bulletin EEI

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Drowsy driving is a major cause of road accidents worldwide, necessitating the development of effective drowsiness detection systems. Each year, there are more accidents and fatalities than ever before for a variety of causes. For instance, there were 22,952 fatalities and 79,545 injuries as a result of nearly 66,500 vehicle accidents in the last 10 years. In this paper, we propose a novel approach for detecting drowsiness based on behavioral cues captured by a digital camera and utilizing the multi-task cascaded convolutional neural network (MTCNN) deep learning algorithm. A high-resolution camera records visual indications like closed or open eye movement to base the technique on the driver's behavior. In order to measure a car user's weariness in the present frame of reference, eyes landmarks are evaluated, which results in the identification of a fresh constraint known as "eyes aspect ratio." A picture with a frame rate of 60 frames per second (f/s) and a resolution of 4,320 eyeballs was used. The accuracy of sleepiness detection was more than 99.9% in excellent lighting and higher than 99.8% in poor lighting, according to testing data. The current study did better in terms of sleepiness detection accuracy than a lot of earlier investigations.

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Section: Literature Reviewmentioning

confidence: 99%

Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning

Majeed,

Hussein Ali,

A. Al-Hilali

et al. 2023

Bulletin EEI

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“…A Raspbian camera is used to capture driver's face and eyes and this captured image is further used to detect driver's fatigue. Driver's drowsiness is detected using brain and visual features (6). EEG and EOG signals are used to extract these features.…”

Section: Literature Reviewmentioning

confidence: 99%

Alcohol Detection Using Automation

Vani¹,

Vijithra²,

Vasan³

et al. 2022

ECS Trans.

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Drunk driving is the condition of driving under the usage of alcohol. A small increase in the blood alcohol level increases the relative risk of a motor vehicle accidents leading to severe injuries and sometimes death. Hence the legislator has set acceptable blood limits for drivers and overruling will lead to legal sentences. Blood Alcohol Content (BAC) is the measure used to check drunken driving. However, this method remains tough to be used due to the huge equipment and maintenance cost. The alcohol detector used in this project consists of MQ3 gas sensor and IoT based NODEMCU. This sensor attached to the steering will sense alcohol content through driver’s breathing. The status of driver along with the location map link will be sent to enrolled phone numbers. FIREBASE and THINGSPEAK features are used to achieve this task. Hence an efficient and cost effective alcohol detection method is implemented in this project.

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“…Its advantages over other microcontroller boards include the ability to connect to screens such as TVs or PC monitors, as well as USB connectivity for keyboards and mice. In addition to Raspberry Pi, the webcam plays a crucial role in the physical distancing detection system [4][5][6][7][8][9][10][11][12][13]. Figure 2.…”

Section: Introductionmentioning

confidence: 99%

Physical Distancing Detection System using OpenCV Based on Raspberry Pi4

Farzan

2022

COELITE

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The Covid-19 pandemic is a disease that is ravaging all over the world, one of which is in our country, namely Indonesia. At this time, officers are still monitoring physical distancing by giving direct warnings to physical distancing violators. Monitoring system that aims to detect physical distancing violations using detection based on biometric identification using faces. The monitoring system uses a Mixio F10 webcam with 1080p resolution which is processed with a Raspberry Pi 4 microcomputer with specifications of 4 GB RAM and an SD Card to store the Raspbian operating system. The distance detection program between faces uses the Python programming language and requires the Open CV library. USB Speaker as an output medium in the form of sound as a warning against physical distancing violators. The tool detects the distance between adjacent faces equipped with a voice indicator if the distance between faces exceeds a predetermined distance with a maximum distance of 3 meters and the level of accuracy of face detection depends on the distance of the face to the camera.

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Fatigue Detection Using Raspberry Pi 3

Cited by 24 publications

References 7 publications

Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning

Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning

Alcohol Detection Using Automation

Physical Distancing Detection System using OpenCV Based on Raspberry Pi4

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