智能交通系統(tǒng)機動車車標識別方法

智能交通系統(tǒng)機動車車標識別方法摘要：隨著城市化進程的加快，機動車數(shù)量急劇增加，交通擁堵、交通事故頻發(fā)已成為社會面臨的嚴重問題。為了提高交通運輸安全水平，智能交通系統(tǒng)應運而生，并得到了廣泛的研究和應用。其中，車輛標識識別技術是智能交通系統(tǒng)中的重要組成部分之一。

本文主要研究智能交通系統(tǒng)中機動車車標識別方法，采用了基于深度神經(jīng)網(wǎng)絡的卷積神經(jīng)網(wǎng)絡（CNN）方法。首先，對機動車車標進行了圖像預處理，包括圖像增強、去噪、二值化等。然后，通過構建車標圖像數(shù)據(jù)庫，利用CNN算法進行特征提取和分類識別，實現(xiàn)了對機動車車標的自動識別。該方法可以有效區(qū)分不同類型的車標，并且對于光照、角度等因素具有較強的魯棒性。

通過實驗驗證，本文所提出的方法具有較高的識別準確性和識別速度，可在智能交通系統(tǒng)中實現(xiàn)對機動車的車標識別，具有廣泛的應用前景和推廣價值。

關鍵詞：智能交通系統(tǒng)；機動車；車標識別；卷積神經(jīng)網(wǎng)絡；深度學習

Abstract:Withtheaccelerationofurbanization,thenumberofvehicleshasincreaseddramatically,andtrafficcongestionandaccidentshavebecomeseriousproblemsfacedbysociety.Inordertoimprovetheleveloftransportationsafety,theintelligenttransportationsystemhasemergedandhasbeenwidelystudiedandapplied.amongthem,theidentificationtechnologyofvehicleidentificationisoneoftheimportantcomponentsintheintelligenttransportationsystem.

Thispapermainlystudiestheidentificationmethodofmotorvehiclelogointheintelligenttransportationsystem,whichadoptstheconvolutionalneuralnetwork(CNN)basedondeepneuralnetwork.Firstly,thevehiclelogoimageispreprocessed,includingimageenhancement,noisereduction,binarization,etc.Then,throughtheconstructionofthevehiclelogoimagedatabase,thefeatureextractionandclassificationrecognitionarerealizedbyusingCNNalgorithm,andtheautomaticrecognitionofthevehiclelogoisrealized.Thismethodcaneffectivelydistinguishdifferenttypesofvehiclelogos,andhasstrongrobustnessforfactorssuchasilluminationandangle.

Throughexperimentalverification,themethodproposedinthispaperhashighrecognitionaccuracyandrecognitionspeed,andcanrealizethelogorecognitionofmotorvehiclesintheintelligenttransportationsystem,whichhasbroadapplicationprospectsandpromotionvalue.

Keywords:intelligenttransportationsystem;motorvehicle;logorecognition;convolutionalneuralnetwork;deeplearningWiththedevelopmentofintelligenttransportationsystems,motorvehiclelogorecognitionhasbecomeanimportantresearchtopicinrecentyears.Thetraditionallogorecognitionmethodbasedonimageprocessingtechnologyhassomelimitations,suchaslowrecognitionrateanddifficulttoadapttocomplexenvironments.

Inthispaper,anewlogorecognitionmethodbasedonconvolutionalneuralnetworkanddeeplearningtechnologywasproposed.Firstly,adatasetofmotorvehiclelogoswasconstructed,andtherawdatawaspreprocessedtoenhancetheimagequality.Then,aconvolutionalneuralnetworkmodelwasdesignedandtrainedusingthedataset.Themodelwasoptimizedbyadjustingthehyperparametersandusingthetransferlearningmethod.Finally,thetrainedmodelwasusedtoidentifylogosinreal-time.

Experimentalresultsshowedthattheproposedmethodachievedhighrecognitionaccuracyandfastrecognitionspeed.Itcaneffectivelyrecognizelogosfromimagescapturedunderdifferentilluminationandangleconditions.Inaddition,themethodisscalableandcanbeappliedtoalargenumberoflogorecognitiontasksinintelligenttransportationsystems.

Inconclusion,theproposedlogorecognitionmethodbasedonconvolutionalneuralnetworkanddeeplearningtechnologyhasgreatpotentialforapplicationinintelligenttransportationsystems.Itprovidesaneffectivesolutionforautomaticlogorecognition,whichcanimprovetheefficiencyandqualityoftrafficmanagementandreducetherisksoftrafficaccidents.Themethodcanalsobeextendedtootherfields,suchasproductrecognition,facerecognition,andobjectdetectionAdditionally,theuseofdeeplearningforlogorecognitionhasthepotentialtorevolutionizethewaybusinessesoperatebyallowingfortheautomationoftasksthatpreviouslyrequiredhumanintervention.Thisincludestaskssuchasmonitoringproductplacementinstores,trackingbrandexposureinmedia,andidentifyingcounterfeitproducts.Thepossibilitiesforimprovedefficiencyandaccuracyintheseareasareendlesswiththeuseofdeeplearningforlogorecognition.

Furthermore,thealgorithmusedinthislogorecognitionmethodcanbecontinuouslyimprovedthroughtheuseoflargerandmorediversedatasets.Astheamountofdataavailablefortrainingincreases,thenetwork'sabilitytoaccuratelyrecognizelogoswillimprove.Thismeansthatthepotentialapplicationsofthistechnologywillonlycontinuetoexpandasmoredatabecomesavailable.

Inconclusion,deeplearning-basedlogorecognitionhasthepotentialtogreatlyimprovevariousaspectsofourdailylivesfromtrafficmanagementtobusinessoperations.Asthetechnologycontinuestoevolveandimprove,wecanexpecttoseeevenmorepracticalusesforlogorecognitioninthefutureOneofthepotentialapplicationsofdeeplearning-basedlogorecognitionisinadvertisingandmarketing.Advertiserscanusethistechnologytotracktheeffectivenessoftheiradvertisingcampaignsbymeasuringtheimpactofdifferentlogosandbrandelementsonconsumerbehavior.Forexample,theycanuselogorecognitiontotrackhowoftentheiradsareviewedandwhichlogosaremosteffectiveatdrivingsales.

Anotherpotentialuseforthistechnologyisinthefieldofsecurity.Deeplearning-basedlogorecognitioncanbeusedtomonitorsecuritycamerasandidentifypotentialthreatsbasedonthelogosthatarecapturedinthefootage.Thiscanhelpsecuritypersonnelrespondtoincidentsmorequicklyandwithgreateraccuracy.

Therearealsoimplicationsforintellectualpropertymanagement.Companiescanuselogorecognitiontomonitortheuseoftheirlogosandtrademarksonline,includingonsocialmediaplatforms.Thiscanhelpthemdetectandpreventtheunauthorizeduseoftheirintellectualproperty,whichcanbeacostlyproblemforbrands.

Furthermore,deeplearning-basedlogorecognitionhasthepotentialtoenhanceaccessibilityforpeoplewithvisualimpairments.Byusingimagerecognitiontechnologytodetectlogosandothervisualcues,applicationscanprovideaudiodescriptionsforpeoplewhomaynotbeabletoseethem.Thiscanmakethecontentmoreaccessibleandhelptocreateamoreinclusivesociety.

Finally,thedevelopmentofdeeplearning-basedlogorecognitionhasthepotentialtocreatenewbusinessopportunitiesinthefieldofartificialintelligence.Asthedemandfordeeplearning-basedapplicationscontinuestogrow,companiesthatspecializeindevelopingandrefiningthealgorithmsandmodelsneededforlogorecognitionwillbewell-positionedtocapitalizeonthistrend.

Overall,deeplearning-basedlogorecognitionhasthepotentialtorevolutionizeawiderangeofindustriesandareasofdailylife.Asthetechnologycontinuestoe