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Thissectionexplainstheproposedmethodfordetectingtirednessindrivers,whichconsistsoffivesteps:

Accordingtoavailablestatistics,over1.3millionpeopledieinroadaccidentseachyear,while20to50millionpeoplesuffer non fatalinjuries.TheNationalHighwayTrafficSafetyAdministration(NHTSA)conservativelyestimatedthat1,00,000vehicle crashesoccureachyearasaresultofdriverdrowsiness,withapproximately1,550deaths[1].AccordingtoIndianenforcement agentspatrollingthemotorwaysandmajorroads,sleep depriveddriversarestillresponsibleforroughly40%ofalltraffic accidents[3].AccordingtotheNationalSleepFoundationoftheUnitedStates,54percentofdeathsarecausedbyadultsdriving avehiclewhiletired,withtheremaining28percentfallingasleep[4].EspeciallyinIndiadrowsydrivingisthemajorproblem. Andthedrowsydrivingisthedangerousconditionofdriving,sleepinessandfatigue. Sleepdifficulties,certaintypesofdrugs, andevenboredom,suchasdriving forlongperiodsoftime,cancausedrowsinessinsituationsofstressandexhaustioninan unexpectedandinconvenientway.Thefeelingofsleepinglowersone'sdegreeofalertness,resultingindangerousconditions and an increased risk of an accident [2]. As a result, the following strategies for detecting sleepiness have been widely employed to prevent catastrophic accidents. They were also divided into three groups. Vehicle based measures were continuallymonitored,includinglaneposition,steeringwheelmovement,andaccelerationpedalpressure.Andthedriver's behaviour is measured, such as yawning, eye closure, eye blinking, and head position. Electrocardiogram (ECG), electromyogram(EMG),electrooculogram(EoG),andelectroencephalogram(EEG)areamongthephysiologicalmeasurements [1].Inthissystemthedrowsinessofthedriverisdetectedbyusingbehaviourmeasures bymonitoringtheconditionofeyes andmovementofthemouthweredeterminedbyusingtheraspberrypiandUSBcamerawhichisusedtorecognizethedrivers drowsinessstatebasedonthresholdvalue.Ifsupposethedrivereyesenclosedformorethan2secondsthebuzzerisactivated anditalertsthedriverandalsoifitcontinuedforthreetimeswithinfiveminutesthenitwillstopthevehicle.Hencethis projectwillhelpinsavingthepreciouslifeofdriverandroadaccidentswillbedecreased.

2. METHODOLOGY

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 01 | Jan 2022 www.irjet.net p ISSN: 2395 0072 © 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page757 Real Time Driver Drowsiness Detection System for Road Safety Gokulraj A P1, Senthil Kumar K L2 1PG Scholar, M.E Industrial Safety Engineering, Department of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode, India 2Professor, Department of Mechatronics, Bannari Amman Institute of Technology, Sathyamangalam, Erode, India *** Abstract Every year, road accidents caused by human error result in an increasing number of deaths and injuries around the world. Drowsy driving has been identified as a significant cause to vehicle accidents. It was demonstrated that driving performance deteriorates with increased drowsiness with resulting crashes constituting more than 20% of all vehicle accidents. Risky driving behaviours can lead to accidents, which can result in significant financial and moral damages. This paper indicates the driver’s drowsiness and prevents them from the fatal accidents. Basically sleeping can be identified from several factors like eye blinking level, yawning, gripping force on the steering, brainwave (EEG) or heartrate (ECG) and so on. This work provides the WEB camera for eye blink and mouth yawn monitoring system and it also gives buzzer to alert the driver during drowsiness and stop the vehicle by applying the brake and clutch with the help of a relay. The system is based on processing the driver’s eyes using Raspberry pi 4 by using a WEB camera that is affixed at the dashboard of the vehicle. This system is capable of detecting facial landmarks, we have utilized the face detection process to detect the iris point and mouth to find the threshold by computing the Eye Aspect Ratio (EAR), Eye Closure Ratio (ECR), Mouth Aspect Ratio (MAR) and Mouth Closure Ratio (MCR) to detect driver’s drowsiness based on adaptive thresholding. In case the observed threshold is met then the brakes of the vehicle are applied and the driver is woken up.

2.1 Capturing Eyecameraismountedonthedashboardofthevehicleanditwillcontinuouslycapturingthefacialimageofthedriver.

Key Words: Eye blink monitoring system, drowsiness, relay, yawning, Raspberry pi 4, iris point.

1. INTRODUCTION

Creating a Region of Interest (ROI)

Afterreceivingvideoinputfromthecamera,wemustfirstconverttheimagetogreyscale,astheOpenCVobjectdetection algorithmrequiresgreyimagesasinput.Todetectthefaces,allofthecolourinformationisnotrequired.Itgeneratesanarray ofdetectionswithx,ycoordinatesandheight,whichisthewidthoftheface.

Finally,thealgorithmdetermineswhetherornotthepersonissleepybycalculatingascore.Thescoreisessentiallya numberthatwillbeusedtodeterminehowlongtheindividualhasremainedclosed eyed.Theautomobilewillbehaltedand thedriverwillbewarnedifthescoreexceedsthethreshold.Theexperiment'sprocessflowisdepictedinFigure1.

TheDlibpackagewasimportedandutilisedtoextractdriverfacelandmarksinthisproposedstudy[13].Thepackageincludes apre trainedfacedetectorthatemploysalinearSVM(supportvectormachine)approachtorecognisefacesandisbasedona variationofthedirectedgradientshistogram.Theapplication'sfacelandmarkswerethenutilisedtomeasuredistancebetween places,andtherealfaciallandmarkpredictorwasbuiltup.TheEARandMARvalueswerecalculatedusingthesedistances[14]. Equation1wasusedtocalculateEAR,whichisdefinedastheratiooftheeye'sheightandbreadth.TheMARwascalculated Fig 1: Flowdiagramoftheproposedalgorithm

3. DRIVER FATIGUE AND ROAD SAFETY

2.5 Calculation

Drowsydriversmayberesponsiblefornearlyathirdofallfatalcaraccidents.Manypeopleareunawarethatfallingasleepat thewheelcausesmoreseriousvehicleaccidentsthandrinking.Whilewehaveallheardofdrunkdriving,wehaven'theard muchaboutsleepydriving,despitethefactthatit'sabighealthandsafetyissue.Whiledistracteddrivinghasreceivedalotof attentionlately,sleepydrivingcontinuestobeamajorcauseofcaraccidents.Inaddition,driversbetweentheagesof16and 24arenearlytwiceaslikelytobeinvolvedinasleepydrivingaccidentasdriversbetweentheagesof40and59.Men(52%) arealsomorelikelythanwomen(30%)toadmittofallingasleepbehindthewheelatsomepoint[12].Long distancedriving hasthedifficultythatmany individualsareunawareof,orprefertoignore,howmuchdrivingistoomuch.Onprolonged journeys,takea15 minuterestoutsidethevehicleeverytwohoursor160kilometres.Thereisnosetguidelineforhowfaryou shoulddriveatanyparticularmoment,butnodestinationisworthputtingyourlifeindangertoget.Don't puttoomuch pressureonyourself.Determineasafedistanceaheadoftimeandpulloverwhenyoureachit.

2.3 Detection of eyes and mouth

Theprocedurefordetectingeyesandmouthsisthesameasfordetectingtheface.First,wesetthecascadeclassifierfor eacheyesandmouthtoextracttheeyesandmouthboundarybox,andthenwecanextracttheeyesandmouthimagefromthe frame. 2.4 Classification Inthisstep,theextractedboundaryoftheeyesandmoutharefeededintotheclassifier.Thentheclassifierwillcategorize whethereyesandmouthareopenorclosed.

4. FACIAL LANDMARK MARKING

Followingthecaptureoffaciallandmarkpoints,theRaspberryPicontrollerreceivestheEARandMARvaluescomputedbythe server,whicharecomparedtothethresholdvalueof0.2(eye)and0.37(mouth).Thecountervalueisincrementedifthevalueis lessthanthethreshold.else,thecountervalueisresettozero.Analertissoundedifthecountervalueexceedsthree.Inaddition, anothervariable(SleepCounter)iskepttrackofhowmanytimestheEARvaluefallsbelowthethreshold.Thevariable(Total Fig -3: FacialLandmarkPointsaccordingtoDliblibrary Table 1: FacialLandmarkspoints

Lefteye [37 42] Righteye [43 48] Mouth [49 68]

5. CLASSIFICATION

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 01 | Jan 2022 www.irjet.net p ISSN: 2395 0072 © 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page759 usingequation2andisdefinedastheratioofthemouth'sheightandbreadth.Thenumeratorrepresentstheeyeandmouth height,whereasthedenominatorrepresentsthebreadthoftheeyeandthemouthashowninthefigure2. (2)(1) (A) (B) Fig -2: (A) Landmarks of Eye in EAR, (B) Landmarks of Mouth in MAR EARandMARfunctioninthesameway.Thedistancebetweentheupperandlowereyelidsisestimatedusingthenumeratorof equation1.Thedenominatorrepresentsthehorizontaldistancebetweentheeyes.Whentheeyesareopen,thenumeratorvalue rises,raisingtheEARvalue;whentheeyesareclosed,thenumeratorvaluefalls,loweringtheEARvalue.EARreadingsare employedinthisscenariotoidentifytirednessofthedriver.TheaverageoftheleftandrighteyesEARvaluesiscalculated.Inour drowsinessdetector,theEyeAspectRatioismonitoredtocheckwhetheritfallsbelowthethresholdvalueanddoesnotriseover itinthenextframe.Asshownbytheaforementionedevent,thepersonhasclosedhisorhereyesandissleeping.IftheEARvalue increasesagain,however,itindicatesthatthepersonhasjustblinkedtheireyeandisnotsleepy.Figure3depictsasetoffacial landmarkpointsdevelopedusingtheDlibtoolandusedtodetermineEAR.Thefaciallandmarkpointsforthelefteye,righteye and mouththatwereemployedinthecomputationarelistedinTable1. Parts Landmark points

International Research Journal of Engineering and Technology (IRJET) e ISSN: 2395 0056 Volume: 09 Issue: 01 | Jan 2022 www.irjet.net p ISSN: 2395 0072 © 2021, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page760 Counter)isusedtocalculatetheECRbystoringthetotalnumberofresponsesreceivedfromthesystem(EyeClosureRatio).The ratiooftheSleepCounterandTotalCountervalueswasdeterminedusingequation3andisdefinedastheratiooftheSleep CounterandTotalCountervalues.Every15successiveframes,theECRvaluewasdetermined(capturedfromcamera).Thetotal countervaluebecomesonewhentheframenumberreaches16,andthesleepcountervaluebecomeszero.Anotificationis generatedintheRaspberrypidisplaywhenevertheECRvalueexceedsthethresholdvalueof0.5,indicatingthatthedriveris drowsyandiftheECRexceedfourtimeswithinthreeminutesthenthecontrolsignalissendtotherelaycircuittostopthe vehicle. (3) 6. EVALUATION OF PERFORMANCE WITH EXPERIMENTAL RESULTS AND DISCUSSION

Thissectionprovidesanempiricalanalysisoftheacquiredresultsaswellasaperformanceevaluationofthesuggestedstrategy.

Thesystembeginsbycollectingreal timedatafromthedrivers.ItthendeterminesthedriverstirednessbycalculatingtheEAR andMARvaluesbasedonthevideocollectedbytheuserandtheraspberrypiresponse.ECRandMCRlevelsarealsousedto identify tiredness. When the face detection algorithm determines that the driver is sleepy, it transmits information to the RaspberryPithroughthecamera,whichactivatesthebuzzerforaround3secondstoalertthedriver andtheraspberrypi displaywillalsoshowthealertmessageonthescreenwhichisshowninthefigures4,5,6and7.Thesystemwillalsoapplies brakeswiththeuseofarelayifthedriverwasnotrespondtothebuzzerforacertainperiodoftimesothatthemotorcanbeset toforwardorbackwardbiastoworkinbothclockwiseandanticlockwisedirectionswith20%efficiencyforroughly7secondsto reducethevehicle'sspeed.

WeusedtheEAR(EyeAspectRatio)andMAR(MouthAspectRatio)andproposedamethodforcalculatingtheECR(EyeClosure Ratio)andMCR(MouthClosureRatio).Whencomparedtopreviousapproachesintheliterature,oursuggestedalgorithm improves accuracy and minimizes the time it takes to compute the values in the Raspberry Pi, resulting in superior consciousnessoutcomesassoonasthedriverfeelstired.Otherinvasivemethods,ontheotherhand,necessitatedtheattachment of a variety of equipment and gadgets to the driver's body, making it impossible for the driver to focus on his driving. Furthermore, with earliertechniques,the driver hadtodo a setup everytime before the ride began. However,the cost of measuringpulserate,heartbeats,andotherbodilyfunctionsishighwiththeseintrusivemethods.HereaRaspberryPi4anda WEBCAMareusedtodetectsleepiness,eliminatingtheexpendituresofmachineryanddisruptionsinthedriver'sfocus.Andthe suggestedalgorithmperformedadmirablyunderfavourablelightingcircumstances.Italsoworksforindividualswhowear glassesandthehardwareattachmentwasalsomorecostefficientandeasilyreplaceableasshowninthefigure8.

7. RESULT AND DISCUSSION

Fig - 4: Normalstate Fig 5: Drowsinessstate Fig – 6: Yawningstate Fig – 7: Distractedstate

[6] Turan,Garima,andSheifaliGupta."RoadAccidentsPreventionsystemusingDriver’sDrowsinessDetection."International JournalofAdvancedResearchinComputerEngineering&Technology(IJARCET)Volume2(2013).

[14] Rezaei,Mahdi,andReinhardKlette."Simultaneousanalysisofdriverbehaviourandroadconditionfordriverdistraction detection."InternationalJournalofImageandDataFusion2,no.3(2011):217 236.

Thegoalofthisprojectistocreateareal timesystemthatmonitorsanddetectsthedriver'ssleepiness,alertsthedriverwithan alarm,andappliesthebrakestostopthevehicle.Non invasivetacticswerefavouredoverintrusiveonestominimizethedriver frombeingdistractedbythesensorslinkedtodriver’sbodywhencomparedtoothersystems.Whendriversareusedtosevere workloadsanddrivingforlongperiodsoftime,thismethodworkswell.Thisisthemosteffectivestrategytoavertaccidentsand savepeople'slives.Thetechnologywillbeabletodetermineiftheeyesandlipsareopenorclosedwhilemonitoring.Awarning signalintheformofabuzzerwillbedeliveredwhentheeyesareclosedforanextendedperiodoftimeandthemouthiskept openedforanextendedperiodoftime.Atthesametime,whenthedriverisdistractedbysomeexternaldiversionthissystem willalertthemtokeeptheireyeonroadandalsoifthedriverstillnotrespondstothebuzzersoundthentheclutchandbrake areactivatedtostopthevehicleslowly.Beforethistheleftandrightindicatorwillbeswitchedontoalerttheothervehicles beforestoppingthevehicle.

[10] Fan, Xiao, Bao Cai Yin, and Yan Feng Sun. "Yawning detection for monitoring driver fatigue." In 2007 International ConferenceonMachineLearningandCybernetics,vol.2,pp.664 668.IEEE,2007.

REFERENCES

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[12] Soukupova,Tereza,andJanCech."Eyeblinkdetectionusingfaciallandmarks."In21stcomputervisionwinterworkshop, RimskeToplice,Slovenia.2016.

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