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一种用于车道线识别的图像灰度化方法.docx

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一种用于车道线识别的图像灰度化方法 Title:AnImageGrayscaleMethodforLaneMarkingDetectioninAutonomousDriving Abstract: Lanemarkingdetectionisacriticalcomponentinautonomousdrivingsystemsasitprovidesvitalinformationforvehiclepathplanningandcontrol.Inthispaper,weproposeanovelimagegrayscalemethodforlanemarkingdetection,aimingtoimprovetheaccuracyandrobustnessoflanerecognitioninvariousdrivingconditions.Theproposedmethodleveragesimageprocessingtechniquestoenhancethevisibilityoflanemarkings,enablingreliableextractionandsubsequentanalysisofthelaneboundaries. 1.Introduction: Autonomousvehiclesrelyonaccurateandreal-timedetectionoflanemarkingstonavigatesafelyonroads.Lanemarkingsprovideessentialguidanceforbothon-roaddrivingandlanechangingmaneuvers.Traditionalapproachesforlanedetectionoftenusecolor-basedsegmentationmethods.However,thesemethodsaresusceptibletovariationsinlightingconditions,weather,androadsurfaceconditions.Inthispaper,wepresentagrayscale-basedapproachthataddressesthelimitationsofcolor-basedtechniques. 2.ImageGrayscaleConversion: TheproposedmethodinvolvesconvertingtheinputRGBimageintograyscalerepresentationtoenhancethevisibilityoflanemarkings.Grayscaleconversioninvolvesweightaveragingofthered,green,andbluecolorchannelsoftheimage.Theresultinggrayscaleimageeliminatescolorvariations,facilitatingamoreconsistentandrobustextractionoflanemarkings. 3.ContrastEnhancement: Tofurtherenhancethevisibilityoflanemarkings,thegrayscaleimageundergoescontrastenhancementusingtechniquessuchashistogramequalization,adaptivehistogramequalization,orcontraststretching.Thesetechniqueseffectivelydistributepixelintensitiesovertheentiregrayscalerange,enhancingthedistinctionbetweenlanemarkingsandthesurroundingroadenvironment. 4.GaussianFiltering: Gaussianfilteringisappliedtothecontrast-enhancedgrayscaleimagetoreducenoiseandcreateasmootherrepresentationoftheimage.Noisereductioniscrucialforaccuratedetectionoflaneboundariesandeliminationoffalsepositivesgeneratedbyimageartifacts. 5.EdgeDetection: Thepreprocessedgrayscal