International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
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DRIVER DROWSINESS DETECTION USING RASPBERRY PI Chirag Patil1, Omkar Thopate2, Soham Patil3, Prof. A.A. Trikolikar4 1,2,3Engineering Student, Dept. Electronics and Telecommunication Engineering, JSPM Imperial College of
Engineering and Research, Wagholi, Pune, Maharashtra, India.
4Professor, Dept. Electronics and Telecommunication Engineering, JSPM Imperial College of Engineering and
Research, Wagholi, Pune, Maharashtra, India. ---------------------------------------------------------------------***--------------------------------------------------------------------dealing with intoxication and drowsiness. By doing this, it Abstract - This abstract describes a Raspberry Pi-based seeks to improve security and avoid catastrophes in a variety of settings, including as driving, using public transportation and the workplace.
system for detecting intoxication and sleepiness. To track vital signs and alcohol levels, it makes use of sensors such a camera, heart-rate sensor, and alcohol sensor. The gathered data is processed by machine learning algorithms, which identify intoxication from alcohol and tiredness. Real-time alerts are produced by the Raspberry Pi, the system's central processing unit, and take the form of auditory notifications or cautions. By offering prompt detection and alarms for tiredness and alcohol impairment, this portable, cost-effective technology seeks to improve safety in workplace and transit situations.
2. LITERATURE SURVEY 1.Bappaditya Mandal, Liyuan Li, Gang Sam Wang, and Jie Lin “Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State” Driver’s fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles due to prolonged driving periods and boredom in working conditions. In this paper, we propose a visionbased fatigue detection system for bus driver monitoring, which is easy and flexible for deployment in buses and large vehicles. The system consists of modules of head-shoulder detection, face detection, eye detection, eye openness estimation, fusion, drowsiness measure percentage of eyelid closure estimation, and fatigue level classification.
Key Words: Drowsiness Detection, Image Processing, Facial Detection, Yawning, Alcohol Detection
1.INTRODUCTION The goal of this project is to come up with a method for waking up sleepy drivers while they are on the road. The tiredness of the driver is one of the factors that contribute to auto accidents. As is well known, there were 1,89,400 road accidents in India in 2018 and 2,01,205 in 2020. According to the research of 2020 traffic accident statistics, 400 fatalities and 1374 accidents occur daily on Indian roadways.
2.Zuojin Li, Liukui Chen, Jun Peng and Ying Wu “Automatic Detection of Driver Fatigue Using Driving Operation Information for Transportation Safety” Fatigued driving is a major cause of road accidents. For this reason, the method in this paper is based on the steering wheel angles (SWA) and yaw angles (YA) information under real driving conditions to detect drivers’ fatigue levels.
Major causes of accidents and fatalities across a variety of industries, including transportation and the workplace, include drowsiness and alcohol impairment. To ensure public safety, it is crucial to identify and treat these disorders immediately. This introduction introduces a cutting-edge method for detecting intoxication and sleepiness using the Raspberry Pi, a flexible and reasonably priced single-board computer.
3. S. Cotter revealed the methodology for the system that records eye movements using the corneal reflection approach in 2011. However, there were significant drawbacks, including the requirement for a headset, which made the method inappropriate and very intrusive.
To record facial images and extract relevant data for drowsiness analysis, the system uses a camera module. An alcohol sensor that detects the quantity of alcohol molecules in the breath is used to detect alcohol. The Raspberry Pi serves as the system's central processing unit, managing the machine learning algorithms and producing immediate notifications whenever alcohol or drowsiness is discovered. These cautions may come in the nature of audio warnings. The major goal of this system is to deliver a cheap, transportable, and trustworthy solution for identifying and
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