Research on Fast R-CNN for Visual Object Detection

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1、硕士留学生学位论文ResearchonFastR-CNNforVisualObjectDetection作者姓名GHULAMARBI学科专业电气与计算机工程指导教师Prof.YUZHULIANG所在学院自动化科学与工程学院论文提交日期2018年5月ResearchonFastR-CNNforVisualObjectDetectionADissertationSubmittedfortheDegreeofMasterADissertationSubmittedfortheDegreeofMasterCandidate

2、:GHULAMARBISupervisor:Prof.YUZHULIANGSouthChinaUniversityofTechnologyGuangzhou,ChinaJune2018AbstractOverthelastdecade,objectdetectionhasemergedasanimportantareaofresearchforcomputervision.Inthisregard,somenovelandimpressingCNNarchitectureshavebeenproposed.Fort

3、hisworkFastR-CNNandVGGNetarchitectureshavebeenincorporatedforthegiventaskofvisualobjectdetection.DifferentproposedmethodshavebeenexercisedandempiricallyitisrealizedthatFastR-CNNarchitectureexhibitsconsiderableperformanceforthegiventask.Inparticularforgivenprob

4、lem,emphasishasbeenrenderedonvisualobjectCapraFalconeri(Markhor),indifferentchallengingenvironmentandbackgroundimages.Themostissueforobjectdetectionusingdeeplearningtechniquesistosavethetrainingandtestingtimewithoutsacrificingitsaccuracy.However,existingFastR-

5、CNNtechniqueprovidegoodaccuracybutitsuffersfrommoretrainingandtestingtimethatlimitstheoverallperformanceofsystem.Inthisthesis,anewdatasetiscreatednamedasMarkhor-VIbyvariousdataaugmentationmethodssuchastranslation,rotation,scalingandsoon.Thenwehavemodifiedexist

6、ingFastR-CNNtechniquebytransferlearningbyvariousmethodsfromlayer1tolayer5.Itisfoundthatconvolutionalobjectdetectionisstillevolvingasatechnology,despiteoutrankingotherobjectdetectionmethods.Itisalsofoundthattransferlearningcansavealotofresourcesiffinetuningispe

7、rformedinaparticularway.Keywords:ArtificialIntelligence,DeepLearning,ObjectDetection,ComputerVision.ITableofContentsAbstract........................................................................................................................................

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