一种基于注意力机制的低光照下行人检测算法-豆柴文库

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一种基于注意力机制的低光照下行人检测算法
Title:Anattention-basedpedestriandetectionalgorithmunderlowlightconditions
Abstract:
Inrecentyears,pedestriandetectionhasgainedsignificantattentionduetoitsimportanceinvariousapplications,suchasautonomousdrivingandsurveillancesystems.However,detectingpedestriansaccuratelyunderlowlightconditionsremainsachallengingtask.Inthispaper,weproposeanattention-basedpedestriandetectionalgorithmthatleveragesthepowerofattentionmechanismstoimprovedetectionperformanceinlowlightenvironments.Theproposedalgorithmincorporatesvarioustechniques,includingfeatureextraction,attentionmechanism,andpost-processing,toachieverobustandaccuratepedestriandetection.Experimentalresultsonpubliclyavailabledatasetsdemonstratethattheproposedalgorithmoutperformsexistingmethodsintermsofdetectionaccuracyandrobustnessunderlowlightconditions.
1.Introduction:
Pedestriandetectionplaysacrucialroleinmanycomputervisionapplications,includingautonomousdriving,videosurveillance,andhuman-computerinteraction.However,detectingpedestriansaccuratelyunderlowlightconditionsisachallengingtask.Inlowlightenvironments,theilluminationlevelisinsufficient,leadingtolowcontrastanddegradedimagequality.Thesefactorsmakeitdifficulttodistinguishpedestriansfromthebackgroundnoise.
2.RelatedWork:
Severalapproacheshavebeenproposedtotacklepedestriandetectionunderlowlightconditions.Existingmethodsoftenrelyonhandcraftedfeatures,suchasHistogramofOrientedGradients(HOG)andLocalBinaryPatterns(LBP).However,thesemethodssufferfromlimiteddiscriminativepowerwhendealingwithlowcontrastimages.Recentadvancesindeeplearninghaveshownpromisingresultsinvariouscomputervisiontasks.Deepconvolutionalneuralnetworks(CNNs)havedemonstratedtheireffectivenessinfeatureextractionforpedestriandetection.However,thesemethodsdonotexplicitlymodeltheattentionmechanism,whichiscrucialforfocusingoninformativeregionsunderlowlightconditions.
3.ProposedMethod:
Theproposedalgorithmconsistsofthreemainstages:featureextraction,attentionmechanism,andpost-processing.
3.1F