基于改进型Faster R-CNN的接触网设备缺陷检测方案-豆柴文库

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基于改进型FasterR-CNN的接触网设备缺陷检测方案
Title:ADefectDetectionSchemeforRailwayOverheadLineEquipmentbasedonImprovedFasterR-CNN
Abstract:
Withtherapiddevelopmentofrailwaytransportation,ensuringthesafeandefficientoperationoftherailwayoverheadlineequipmenthasbecomecrucial.ThispaperproposesadefectdetectionschemeforrailwayoverheadlineequipmentbasedonanimprovedFasterR-CNN.Theproposedschemeaimstoovercomethelimitationoftraditionaldefectdetectionmethodsbyleveragingthepowerofdeeplearningtechniquesandshapefeatures.
Keywords:defectdetection,railwayoverheadlineequipment,FasterR-CNN,deeplearning.
1.Introduction
Railwayoverheadlineequipmentplaysavitalroleinensuringthesafetyandstabilityofrailwaytransportationsystems.However,theexistenceofdefectsintheseequipmentmayjeopardizetheoperationandleadtopotentialaccidents.Therefore,areliableandefficientdefectdetectionsystemisrequiredtoidentifypotentialdefectsaccuratelyandtimely.Traditionaldefectdetectionmethodsrelyheavilyonmanualinspection,whichislabor-intensive,time-consuming,andpronetohumanerrors.
2.RelatedWork
Inrecentyears,deeplearning-basedobjectdetectionmodels,suchasFasterR-CNN,haveachievedremarkableperformanceinvariousobjectdetectiontasks.However,applyingthesemodelstothedefectdetectionofrailwayoverheadlineequipmentfaceschallengesduetothecomplexandirregularshapeoftheequipment.
3.ProposedScheme
Inthispaper,weproposeanimproveddefectdetectionschemeforrailwayoverheadlineequipmentbasedontheFasterR-CNNframework.Theschemeconsistsofthefollowingkeysteps:
3.1DatasetPreparation
Totrainandevaluatetheproposedscheme,adatasetoflabeledimagesofrailwayoverheadlineequipmentwithvarioustypesofdefectsisprepared.Thedatasetshouldcoverdiverselightingconditions,perspectives,anddefecttypestoenhancethemodel'sgeneralizationability.
3.2RegionProposalGeneration
Tohandletheirregularshapeofrailwayoverheadlineequipment,animprovedregionproposalgenerationalgorithmisdeveloped.Thisalgorithmcombinestraditionaledgedetectiontechniqueswithshape-basedmethodstoaccuratelygenerateregionpropos