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基于轮廓特征及灰度相似度匹配的接触网绝缘子缺陷检测（英文）.docx

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基于轮廓特征及灰度相似度匹配的接触网绝缘子缺陷检测（英文） Title:ContactNetworkInsulatorDefectDetectionBasedonContourFeaturesandGrey-levelSimilarityMatching Abstract: Thedetectionofdefectsincontactnetworkinsulatorsplaysacrucialroleinensuringthesafeandefficientoperationofrailwaysystems.Inthispaper,weproposeanovelapproachthatcombinescontourfeaturesandgrey-levelsimilaritymatchingforcontactnetworkinsulatordefectdetection.Theproposedmethodcaneffectivelyidentifyandclassifyvarioustypesofdefects,includingcracks,breakages,contaminants,andabnormalshapes.Experimentalresultsdemonstratethatourmethodachieveshighaccuracyandrobustnessindefectdetection,therebyprovidingvaluableinsightsformaintainingtheintegrityofcontactnetworkinsulatorsinrailwaysystems. 1.Introduction Thecontactnetworkisanessentialcomponentofrailwaysystems,andinsulatorsarewidelyusedtoprovideinsulationandsupportforthenetwork.However,duetovariousenvironmentalandoperationalfactors,contactnetworkinsulatorsaresusceptibletodefectsthatmaycompromisesystemperformanceandsafety.Hence,developinganefficientandaccuratedefectdetectionmethodforcontactnetworkinsulatorsisofparamountimportance. 2.RelatedWork Previousresearchoncontactnetworkinsulatordefectdetectionhasmainlyfocusedonimageprocessingtechniques,includingedgedetection,textureanalysis,andsegmentationalgorithms.However,thesemethodsoftensufferfromlimitationsintermsofaccuracy,robustness,andcomputationalefficiency.Therefore,thereisaneedforanewapproachthatcanaddressthesechallenges. 3.ProposedMethod Ourproposedmethodforcontactnetworkinsulatordefectdetectioncombinescontourfeaturesandgrey-levelsimilaritymatching.Firstly,contourfeaturesareextractedfromtheinputimageusingedgedetectionalgorithmstohighlightpotentialdefects.Next,thegrey-levelsimilaritybetweentheextractedcontourandareferencemodeliscomputedtoclassifyandidentifythespecifictypeofdefect.Thereferencemodelisconstructedusingasupervisedlearningapproach,whereasetofdefect-freeinsulatorimagesisusedtodefinethenormalrangeofgrey-levelsimilarity.Anydeviationsfromthis