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适用于无人机巡检图像的输电线路螺栓检测方法.docx

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适用于无人机巡检图像的输电线路螺栓检测方法 Abstract Withtheincreasingapplicationofunmannedaerialvehicles(UAVs)inpowerlineinspection,thereisagrowingdemandforefficientandaccuratedetectionmethodsforboltdefects.Inthisstudy,weproposeaboltdetectionmethodbasedonUAVinspectionimages,whichsolvestheproblemoflowefficiencyandlowaccuracyintraditionalmanualdetectionmethods.Themethodadoptsdeeplearningalgorithmstoautomaticallyextractboltfeatures,combinedwithimagesegmentationanddefectlocalizationtechniques,toachieveboltdetectionanddefectclassification.Theexperimentalresultsshowthattheproposedmethodhashighaccuracyandcaneffectivelyreducetheworkloadofmanualdetection. Keywords:unmannedaerialvehicle,powerlineinspection,boltdetection,deeplearning,defectclassification Introduction Powertransmissionlinesareavitalpartofthepowergridandplayanimportantroleinthenationalenergyinfrastructure.Duetotheharshworkingenvironment,high-altitude,andlongdistanceofthetransmissionlines,traditionalmanualinspectionmethodsarenotonlytime-consumingandlabor-intensivebutalsohavehighrisksofaccidents.WiththerapiddevelopmentofUAVs,theyhavebecomeincreasinglypopularinpowerlineinspectionduetotheiradvantagesoflowcost,highefficiency,andhighsafety. Boltsareacrucialpartofpowertransmissionlinesthatconnecttheconductorsandthetowers,andtheirsafetyperformancedirectlyaffectsthesecurityandstabilityoftheentiretransmissionline.Thetraditionalmanualdetectionmethodforboltdefectsreliesonskilledpersonnel,whichnotonlyhaslowefficiencybutalsocannotguaranteehighaccuracy.Therefore,thereisanurgentneedforanefficientandaccuratedetectionmethodforboltdefectsinpowertransmissionlines. Methodology Theproposedboltdetectionmethodiscomposedofthreesteps,asshowninFigure1,includingimagepreprocessing,featureextraction,anddefectdetection. Imagepreprocessing:TherawUAVinspectionimagescontainvarioustypesofnoiseandinterference,whichwillaffecttheaccuracyofthesubsequentfeatureextractionanddefectdetection.Therefore,imagepreprocessingisnecessarytoenhancethequalityoftheimage.Inthisstudy,wea