基于DCN-Mobile-YOLO模型的多车道车辆计数-豆柴文库

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基于DCN-Mobile-YOLO模型的多车道车辆计数
Title:ObjectCountinginMultipleLanesusingDCN-Mobile-YOLOModel
Abstract:
Objectcountingisasignificantchallengeinthefieldofcomputervisionduetoitswidespreadapplicationsintrafficmonitoring,surveillancesystems,andobjecttracking.ThispaperproposesanovelapproachforvehiclecountinginmultiplelanesusingtheDCN-Mobile-YOLO(DeformableConvolutionalNetworks-MobileYouOnlyLookOnce)model.TheproposedmethodleveragestheadvantagesofboththeDCNmoduleforimprovedfeaturerepresentationandtheYOLOarchitectureforreal-timeobjectdetection.Experimentalresultsonbenchmarkdatasetsdemonstratetheeffectivenessandefficiencyoftheproposedapproachinaccuratelycountingvehiclesinmultiplelanes.
1.Introduction
Objectcountinginmultiplelanesisacriticaltaskinvariousapplicationssuchastrafficmonitoring,congestionmanagement,andintelligenttransportationsystems.Theabilitytoaccuratelycountvehiclesinmultiplelanesisessentialforensuringeffectivetrafficcontrolandmanagement.Traditionalobjectcountingmethodsoftensufferfromlowaccuracyandcomputationalinefficiency.Thus,thereisaneedforarobustandefficientapproachthatcanaddresstheselimitations.ThispaperproposestheuseoftheDCN-Mobile-YOLOmodelforvehiclecountinginmultiplelanes.
2.RelatedWork
Thissectionprovidesanoverviewofexistingobjectcountingmethods.Itdiscussestraditionalapproachessuchasbackgroundsubtraction,opticalflow,andtracking-basedmethods.Italsoreviewsrecentdeeplearning-basedsolutionsforobjectcounting,includingFasterR-CNN,SSD,andYOLO.Thelimitationsandadvantagesofthesemethodsareanalyzed,highlightingtheneedforaspecializedmodelforvehiclecountinginmultiplelanes.
3.DCN-Mobile-YOLOModel
ThissectionintroducestheDCN-Mobile-YOLOmodel,whichcombinesthebenefitsoftheDCNmoduleandtheYOLOarchitecture.TheDCNmoduleenhancestheabilityofthemodeltocapturedeformablefeaturesbyintroducingspatialdeformableconvolutions.TheYOLOarchitectureenablesreal-timeobjectdetectionthroughasingleforwardpassofthenetwork.ThedesignandarchitectureoftheDCN-Mobile-YOLOmodelaredescribedindetail.
4.Vehic