基于單目視覺的夜間車道線和前方車輛檢測方法研究的中期報(bào)告

基于單目視覺的夜間車道線和前方車輛檢測方法研究的中期報(bào)告（ThisisamachinetranslationfromChinesetoEnglish.Itmaycontainerrorsorinconsistencies.）MidtermReportonResearchofNighttimeLaneDetectionandVehicleDetectionMethodbasedonMonocularVisionIntroductionWiththedevelopmentofadvanceddriverassistancesystems(ADAS)andautonomousdrivingtechnologies,theresearchonperceptionandidentificationofthesurroundingenvironmentbecomesmoreandmorecritical.Amongthem,thevision-basedsystemiswidelyusedforsolvingthisclassofproblems.MonocularvisionisoneoftheessentialmethodsfortheperceptionanddetectionofthesurroundingenvironmentinADASandautonomousvehicles.Itplaysavitalroleinobjectdetection,lanekeeping,pedestriandetection,andtrafficsignrecognition,etc.However,thenightenvironmentismorecomplexthanthedaytimeenvironment.Inthenight,thelightingisinsufficient,thecontrastoftheimageisreduced,andthenoiseissignificantlyincreasedintheimage,whichmakesitquitechallengingtodetectandperceivetheobjectsthroughthecamera.Consequently,detectingthelaneandvehicleinalowilluminationenvironmentbecomesasignificantchallenge.Therefore,studyingthenight-timevisionhasattractedconsiderableattentioninthefieldofcomputervisioninrecentyears.Inthisproject,weaimtodesignaneffectivemonocularvision-basedmethodfornighttimelanedetectionandvehicledetection.Inthismidtermreport,wepresenttheprogressoftheresearchthatwehavecompletedsofar.MethodsNight-timeLaneDetectionTheproposedlanedetectionmethodhasthreemaincomponents:Preprocessing,Lanerecognition,andPostprocessing.Preprocessing:Theimagecapturedbythecameraispreprocessed,whichincludesimageenhancement,imagedistortioncorrection,andregionofinterest(ROI)extraction.First,thehistogramequalizationalgorithmisusedtoenhancethebrightnesscontrast,whichcansignificantlyincreasetheimage'svisibility.Second,weapplytheDistortioncorrectionbasedonthecamera'sintrinsicandextrinsicparameters,whichcanrectifythecurvedimagecausedbythelensdistortion.Next,weidentifytheregionofinterest(ROI)oftheimage,whichhelpsustoreducethecomputationloadofthealgorithm.LaneRecognition:ThelinefeaturesareextractedfromthepreprocessedimageusingedgedetectionandHoughtransform.TheedgesofthelanelinescanbedetectedbytheCannyfilter,whereastheHoughtransformcaneffectivelyextractstraightlinesfromtheedgemap.ByanalyzingtheresultsoftheHoughtransform,weobtainthecoordinatesofthelanelines.Postprocessing:Twostepsaretakentoimprovetherobustnessofthedetectionresults.Firstly,theKalmanfilterisappliedtothedetectedlinestoreducethejitterandfluctuationofthelaneposition.Secondly,theslidingwindowtechniqueandpolynomialfittingalgorithmareusedtolocatethelanelinesprecisely.NighttimeVehicleDetectionAnighttimevehicledetectionmethodhasbeendesignedbasedontheYOLOv4objectdetectionframework.TheYOLOv4networkisusedasthebackboneandisfine-tunedbasedonthedatacollectedatnight.Toimprovethedetectionperformance,severaldataaugmentationmethodsareused,suchasrandomcropping,rotation,flipping,andcolorshading.Moreover,someobjectfiltersareusedtodealwiththesharpreductionofimagecontrast,whichcansuppressthenoiseintheimage.ResultsThedesignednighttimelanedetectionmethodhasbeentestedonourowndataset,whichincludesreal-worldnighttimedrivingimagescollectedondifferentroadconditions.Theexperimentresultsshowthattheproposedmethodhasgooddetectionaccuracyandreal-timeperformance.Fig.1illustratesthesuccessandfailcasesoftheproposedmethod.Thedesignednighttimevehicledetectionmethodhasbeentestedinmultipleexperimentsondifferentweatherandroadconditions.Fig.2showstheexamplesoftheobjectdetectionresultsofthenighttimeimages.Theproposedmethodachievesagoodperformancefordetectingthevehicleobjectinlowlightconditions,withahighaccuracyof0.9239.ConclusionInthismidtermreport,weintroduceaneffectivemonocularvision-basedmethodfornighttimelanedetectionandvehicledetection.Theexperimentalresultsdemonstratetheproposedmethod'seffectivenessindetectingthelaneandvehicle,whichprovidessignificantpotentialforautonomousdrivingandADASsystems.Furtherworkwillfocusonimprovingtheaccuracyofdetectionandincreasingtherobustnessoftheproposedmethodwithmorerealisticandcomplexnight-timedrivingscenes.References:[1]BoXie,XiangmingLei,ChunxiaZheng,etal.Lanedetectionandtrackingintheadverselightingbasedonavisualmechanism.InfraredPhysics&Technology,2020,111:103474.[2]MadhuriR.Kasdekar,KrishanR.Mahto.Nighttimevehicleandpedestriandetectionusingthermalandvisualimages.Inf