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基于深度学习的计算机图像识别技术研究.docx

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基于深度学习的计算机图像识别技术研究 Abstract Deeplearninghasbeenacrucialareaofresearchinthefieldofcomputervision.Ithasrevolutionizedtheimagerecognitiontechnologyandhasfoundapplicationsinvariousdomainslikemedicalimaging,autonomousvehicles,surveillance,andsecuritysystems.Thispaperpresentsacomprehensivestudyofdeeplearning-basedimagerecognitiontechniques.WediscussthepopulardeepnetworkarchitectureslikeConvolutionalNeuralNetworks(CNNs),RecurrentNeuralNetworks(RNNs),andtheirvariantslikeResNet,VGG,andLSTM.Wealsoexplorethechallengesintrainingdeepnetworks,andseveralrecentadvancesinnetworkoptimization,regularization,anddataaugmentationtechniques.Finally,wepresentsomerecentapplicationsofdeeplearninginimagerecognitionanddiscussthefuturedirectionsinthefield. Introduction Imagerecognitionistheprocessofidentifyingobjects,scenes,orpatternsindigitalimages.Ithasnumerousapplicationsinvariousfieldslikehealthcare,autonomousvehicles,surveillance,andsecuritysystems.TraditionalimagerecognitionmethodsrelyonhandcraftedfeaturesandshallowmachinelearningmodelslikeSVM,RandomForests,andNaiveBayes.However,thesemethodshavelimitationsinprocessingcomplexanddiverseimages.Recently,deeplearninghasemergedasapromisingapproachtoovercometheselimitationsandachievestate-of-the-artperformanceinimagerecognitiontasks. Deeplearningreferstoasubsetofmachinelearningtechniquesthatusedeepneuralnetworkstomodelhigh-levelabstractionsindata.Thesenetworksconsistofmultiplelayersofinterconnectednodesthatmimicthestructureofthehumanbrain.Withtherecentadvancesincomputingpower,dataavailability,andalgorithmicdevelopments,deeplearninghasachievedremarkableresultsinvariousdomainslikeimagerecognition,speechrecognition,naturallanguageprocessing,androbotics. DeepLearning-BasedImageRecognitionTechniques ConvolutionalNeuralNetworks ConvolutionalNeuralNetworks(CNNs)arethemostpopulardeepnetworkarchitectureforimagerecognition.Theywereinspiredbythestructureofthehumanvisualsystemandconsistofmultiplelayersofconvolutionalandpoolingoperationsfollowedbyfullyconnec