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基于生成对抗网络与ICNet的羊骨架图像实时语义分割.docx

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基于生成对抗网络与ICNet的羊骨架图像实时语义分割 Title:Real-TimeSemanticSegmentationofSheepSkeletonImagesBasedonGenerativeAdversarialNetworksandICNet Abstract: Semanticsegmentationplaysacrucialroleinvariouscomputervisionapplications,includingobjectdetection,sceneunderstanding,androbotics.Inthispaper,weproposeanovelapproachforreal-timesemanticsegmentationofsheepskeletonimagesusingacombinationofGenerativeAdversarialNetworks(GANs)andICNet.Theproposedframeworknotonlyaccuratelypredictsthesemanticlabelsbutalsoachievesreal-timeperformance.Experimentalresultsonasheepskeletondatasetdemonstratetheeffectivenessandefficiencyofourapproach. 1.Introduction Thetaskofsemanticsegmentationinvolvesassigningspecificlabelstoeachpixelinanimage.Accuratesegmentationofsheepskeletonimagesisessentialforanimalbehavioranalysisandidentificationofskeletalanomalies.However,existingapproachesoftensufferfromlimitationsintermsofaccuracyorreal-timeperformance.Toovercometheselimitations,weproposeanovelapproachthatcombinesthepowerofGANsandICNet. 2.RelatedWork Previousstudieshaveextensivelyexploreddifferenttechniquesforsemanticsegmentation.Manydeeplearning-basedapproaches,suchasFullyConvolutionalNetworks(FCN)andU-Net,haveshownimpressiveresults.Additionally,GANshavebeensuccessfullyemployedforimagegenerationanddomainadaptationtasks.ICNet,amulti-scalearchitecture,hasbeenwidelyusedforreal-timesegmentation.However,nopriorworkhasspecificallyaddressedthereal-timesemanticsegmentationofsheepskeletonimages. 3.ProposedMethod Ourapproachinvolvesatwo-stepprocess:skeletongenerationusingGANsandsemanticsegmentationusingICNet. 3.1SkeletonGenerationusingGANs WeleveragethecapabilityofGANstogeneraterealisticsheepskeletonimages.Thegeneratornetworkistrainedtoproduceskeletonimagesfromrandomnoisevectors,whilethediscriminatornetworklearnstodifferentiatebetweenrealandgeneratedskeletonimages.BytrainingtheGANs,weensurethatthegeneratedskeletonimagescloselyresembletherealskeletonimages. 3.2SemanticSegmentationusingICNet ICNetisamulti-scalearchitecturethath