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Driver Drowsiness Detection System for Accident Prevention
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 06 | June 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 744 Driver Drowsiness Detection System for Accident Prevention Akhil Prabha1, Abhishek Manikandan2, Anandu Vijayan3, Mr. Shafan Salam4 1,2,3Electronics and Communication, Ilahia College of Engineering and Technology, Mulavoor, Kerala, India. 4Professor, Electronics and Communication, Ilahia College of Engineering and Technology, Mulavoor, Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – Drowsiness is one of the main reasons that causes accidents, most of the times driver become drowsy when driver is sleep deprived, drinking alcohol, stress, bad posture while driving, sleep disorders and many more. Proposed system consists of image-based drowsinessdetection using driver eyes and mouth, along with continuous alcohol consumption detection. When eye closed for above a certain duration then it can be considered as driver in drowsy condition. Also, yawning denotes the tendency to drowsiness. As mentioned before that alcohol consumption isalsoareason for road accidents by making driver into the drowsy state. Alcohol presence is continuously monitored using sensors and whenever alcohol detects vehicle engine needstoshutdownto avoid accidents. Key Words: Drowsiness, Alcohol, Driver, Eye, Yawning, Sensors. 1. INTRODUCTION Driver drowsiness detection andalcohol detectionareuseful tools to avoid the possibility of leading to accidents. Proposed system can be placed in electric vehicles (but not limited to) because speed of the vehicle will be controlled easily whenever drowsiness and alcohol presence detected. Drowsiness is detected by monitoring facial features over a camera which pointing towards the driver. Using machine learning driver’s face is converted into 68 salient points. It includes both mouth and eye coordinates, by detecting and analysing these eye points, drowsiness detection ispossible. Whenever the driver feels drowsy, there will be chances their eyes get closed. By detecting the eyes are closed for a certain duration indicates driver might be in drowsy state. When drowsiness detected vehicle speed will be reduced automatically and alert produced to wake the driver. Continuous alcohol detection will be useful to avoid accidents due to drink and drive, whenever the alcohol presence above allowed level then vehicle will be stopped. 1.1 Drowsiness detection Using a camera driver face activity will be continuously monitored in real time. Using software libraries such as dlib, OpenCv and numpy are used for face recognition, eye and mouthcoordinates identification.ParameterslikeEyeAspect Ratio (EAR)and Mouth Aspect Ratio (MAR) are derivedfrom equations with respect to the coordinate values. EAR and MAR values are variable based on the facial expressions. Whenever eye is closed EAR value is lesser and when eye is open EAR value will be high.A threshold EAR value is usedto determine whether eyes are shut or open. If EAR value consecutively below threshold value then system assumes that driver is in drowsy stateandalertsthedriverandvehicle speed is reduced. Yawning can be detected using MAR values, like the EAR, a threshold MAR is used to detect the yawning. Whenever the MAR value is below threshold MAR then mouth is closed, MAR value above threshold valueindicatesthatthemouth of the driver is opened. The threshold MAR will be picked to ensure the talking of the driver is not mis-triggered as yawning. 1.2 Alcohol Detection Alcohol consumption of thedriverwill bedetectedbyusinga MQ3 sensor. If driver consumed alcohol, then alcohol level inside the vehicle will be higher than a threshold value. When alcohol consumption detected, then the vehicle immediately stops to avoid risk of accident. 2. SYSTEM DESIGN System consists of camera, image processing part , alcohol detection part, speed controller and alert generator. Camera is used to continuously monitors the face activity of the driver. The image processingpart estimatetheEARandMAR values to determine drowsiness. Along with drowsiness detection alcohol level insidethe vehicle is determinedusing gas sensor. Speed controller is provided to vary the speed of electric vehicle when drowsiness or alcohol presence is determined. Eye coordinates are determined during image processing and each eye is represented by 6 distinctpoints.EARvalueis calculated using these eye coordinatesvalues,seefigure1.In case the EAR value is less than the threshold value for consecutive frames then system detects that the driver is drowsy. Similarly based on the mouth coordinates MAR value is determined. The MAR value above threshold MARis defined to be yawning condition. An Alert generatingsystem gives audible warning signal to driver when drowsiness detected by system.
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 06 | June 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 745 Fig -1: Eye Coordinates EAR value is determined by, EAR = ( P2− P6 ) + ( P3−P5 ) / 2 *( P1−P4 ) Fig -2: Mouth coordinates Also Mouth Aspect Ratio (MAR) is determined by, MAR= (|| P2-P8 ||+|| P3-P7 ||+|| P4 - P6 || ) / 2 ( ||P1-P5|| ) 2.1 Existing System System captures real time feed of the driver while driving and using Haar cascade algorithm face can be recognized. From the outline of face, eye and mouth will be detected. Each frame of the camera feed is used to measure EAR .With optimized algorithm eye shut due to sleepiness is detected. All other unintentional events like blinking of eye will notbe considered by the system as drowsiness event. In other words, it is assumed to be driver is in drowsy condition, so false detection will be avoided. After detecting drowsy state of driver alert signal is passed from the signal to alert the driver. System workflow is given below. Step 1: Start Step 2: Camera Initialization by the IPU Step 3: Camera starts to fetch images Step 3: Detecting facial landmarks Step 4: Detecting eye Coordinates Step 5: Finding EAR value Step 6: Comparing with threshold values Step 7: If drowsiness event detected, alert is produced Step 8: Else, continue monitoring Fig -3 : Block diagram of existing system 2.2 Proposed System Along with driver drowsiness detection using EAR, new system consists of Yawn detection, Alcohol detection and vehicle speed control. Yawn detection is done by using MAR threshold value, if the MAR value is over a predefined threshold, then it is assumed that driver is yawning. Alcohol consumption monitoringrunscontinuouslytodeterminethe weather driver consumed alcohol or not. If system detects the alcohol consumption event, thenthevehicleimmediately get stopped to prevent him/her continue driving. Whenever drowsiness state of driver determined then vehicle speed reduces automatically to halt along with audible alert .
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 06 | June 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 746 Fig -4: Block Diagram of Proposed System Raspberry pi is used for image processing of the real time feed from the camera. Separate Arduino UNO is used for alcohol detection part, for reducing load of Raspberry Pi, during prototyping phase. With better software optimization, the functions of Arduino can be shift into Raspberry Pi platform, thus removing Arduino altogether from system. This optimization is part of the future scope. MQ3 sensor used as the alcohol sensor here and Motor Driver indicates vehicle speedcontroller,botharecontrolled and monitored by the Arduino. Buzzers are provided to indicate alcohol presence and drowsiness. As future enhancement this system can adopt to autopilot related automation in electric vehicles. i.e., whenever the driver in drowsy condition then EV shift to autopilot mode automatically. IR based Night vision camera is analternative over normal camera for efficient functioning of system during night time with zero cabin light. 2.3 Results Fig -5: Normal Condition Fig -6: Yawn Detection Fig -7: Drowsiness Detection 3. CONCLUSIONS Currently Driver drowsiness system is majorly enabled on high end cars, which makes the advantage of this safety to not reach common man. With proposed project the safety can be bring to on all types of vehicles with less cost, which make the technology accessible. While monitoring is in progress the risky situations like microsleep & drowsiness are proactively identified and accidents can be avoided. Alcohol level of the driver is monitored to avoid driving while the driver is intoxicated. By doing this many accidents can be prevented and gives safety to driver, passengers and pedestrians. ACKNOWLEDGEMENT The main motivation and driving force behind the work is Mr. Shafan Salam (Asst. Professor, Electronics and
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 09 Issue: 06 | June 2022 www.irjet.net p-ISSN: 2395-0072 © 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 747 Communication Engineering, Ilahia College of Engineering and Technology, Muvattupuzha, Kerala). I am unboundedly grateful for his valuable corrections and guidance that helped improving this work. REFERENCES [1] Eye Detection Using Morphological and Color Image Processing Tanmay Rajpathaka, Ratnesh Kumar and Eric Schwartzb [2] A Robust Algorithm for Eye Detection on Grey Intensity Face without Spectacles- JCS&T Vol. 5 No. 3 [3] Froba Kebbuck: Audio- and Video-Based Biometric Person Authentication,3rdInternational Conference,AVBPA 2001, Halmstad, Sweden, June 2001.Proceedings, Springer. ISBN 3- 540- 42216-1. [4] Driver Drowsiness Detection using Eye-Closeness Detection (2016 12th International Conference on Signal- Image Technology & Internet-Based Systems)
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