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改进YOLOv3算法及其在安全帽检测中的应用.docx

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改进YOLOv3算法及其在安全帽检测中的应用 Introduction Withthedevelopmentofcomputervisiontechnology,objectdetectionhasbecomeahotresearchtopic.Manyalgorithmshavebeenproposedtodetectdifferenttypesofobjects.Amongthesealgorithms,YOLO(YouOnlyLookOnce)isareal-timeobjectdetectionalgorithm,whichcanaccuratelyandquicklydetectobjectsinimagesandvideos.Inthispaper,wewillimprovetheYOLOv3algorithmandapplyittothedetectionofsafetyhelmets. Background YOLOwasfirstproposedbyJosephRedmonin2015,andhassinceevolvedintothreeversions.YOLOv3isthelatestversion,whichhasahigheraccuracyanddetectionspeedthanYOLOv2.Itcandetectobjectsinreal-timeatanaveragespeedof30framespersecond. Safetyhelmetdetectionisanimportanttaskinthefieldofindustrialsafety.Safetyhelmetscanprotectworkersfromheadinjuriescausedbyfallingobjects,collisions,orotherworkplacehazards.Therefore,detectingwhethertheworkerwearsasafetyhelmetiscrucialforensuringtheirsafety.Traditionaldetectionmethodsrelyonmanualinspection,whichistime-consuminganderror-prone.Therefore,afast,reliable,andaccurateautomatedsafetyhelmetdetectionmethodisnecessary. Methods TheYOLOv3algorithmisbasedonaconvolutionalneuralnetwork,whichcaneffectivelyextractfeaturesofobjectsinimagesandclassifythem.However,theoriginalYOLOv3algorithmhassomelimitationsthataffectitsdetectionaccuracy,suchassmallobjectdetection,objectocclusion,andlow-contrastimages.Toovercometheselimitations,weproposedseveralimprovementstotheYOLOv3algorithm: 1.FeaturePyramidNetwork(FPN) WeaddedaFeaturePyramidNetwork(FPN)totheYOLOv3architecture.FPNisamulti-scalefeatureextractorthatcanextractfeaturesatdifferentresolutions.ByusingFPN,wecaneffectivelydetectobjectsofdifferentsizes,andimprovethedetectionaccuracyofsmallobjects. 2.K-meansclustering WeusedtheK-meansclusteringmethodtotunetheanchorboxesintheYOLOv3algorithm.Anchorboxesarethereferenceboxesthatareusedtopredictthepositionandsizeofobjectsintheimage.ByusingK-meansclustering,wecangeneratemoreaccurateanchorboxes,whichcanimprovethedetectionaccuracy. 3.Color-spaceaugmentation Weapp