一种面向非受控场景的视频船舶水尺识别方法-豆柴文库

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一种面向非受控场景的视频船舶水尺识别方法
Title:AnUnsupervisedVideoShipRulerRecognitionMethodforNon-controlledScenarios
Abstract:
Therecognitionofshiprulersplaysacrucialroleinvariousapplicationssuchasmaritimesurveillance,navigation,andshipmonitoring.However,mostexistingshiprulerrecognitionmethodsheavilyrelyonsupervisedlearningalgorithms,whichrequirealargeamountofannotatedtrainingdata.Inreal-worldscenarios,whereshiprulersmayappearindifferentenvironmentsandconditions,itisoftenchallengingtocollectsufficientannotateddatafortraining.Toaddressthisissue,thispaperproposesanunsupervisedvideoshiprulerrecognitionmethodthatcaneffectivelyrecognizeshiprulersinnon-controlledscenarios.
1.Introduction
Therecognitionofshiprulersisnecessaryforaccuratemeasurements,whicharecrucialforseveralmaritimeapplications.Innon-controlledscenarios,shipsmayappearinvaryingenvironmentalconditions,makingitdifficulttoemploytraditionalsupervisedlearningmethods.Thispaperaimstodevelopanunsupervisedvideoshiprulerrecognitionmethodthatdoesnotrequireannotatedtrainingdata.
2.RelatedWork
Thissectionreviewsexistingliteratureonshiprulerrecognitionmethods,includingsupervisedlearningapproaches,feature-basedmethods,anddeeplearningtechniques.Itdiscussestheirlimitationsinnon-controlledscenariosandmotivatestheneedforanunsupervisedapproach.
3.Methodology
Theproposedmethodconsistsofseveralsteps:
3.1.ShipDetection
Shipdetectionisperformedtolocateshipsinthevideoframes.Thiscanbeachievedusingtraditionalcomputervisiontechniquesordeeplearning-basedobjectdetectionalgorithms.
3.2.ShipSegmentation
Onceshipsaredetected,theproposedmethodsegmentstheshipsfromthebackground.Severalimagesegmentationtechniquescanbeused,includingthresholding,graphcuts,orregion-basedmethods.
3.3.ShipRulerExtraction
Aftershipsegmentation,theshiprulersneedtobeextractedfromtheshipregions.Thiscanbeachievedbyanalyzingthegeometricpropertiesofships,suchasstraightlinesorrectangularshapes.
3.4.RulerVerification
Toensuretheaccuracyofshiprulerrecognition,averificationstepisperformed.Thiss