基于改进YOLOv4的水下垃圾清理机器人视觉检测算法（英文）-豆柴文库

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基于改进YOLOv4的水下垃圾清理机器人视觉检测算法（英文）
Title:Vision-basedDetectionAlgorithmforUnderwaterGarbageCleanupRobotusingEnhancedYOLOv4
Abstract:
Theincreasingpollutionofoceansandotherwaterbodieswithgarbageposesasignificantthreattomarinelifeandtheecosystemasawhole.Totacklethisissue,thedevelopmentofunderwatergarbagecleanuprobotshasgainedconsiderableattention.Theserobotsutilizeadvancedcomputervisiontechniquestodetectandcollectdebrisefficiently.Thispaperproposesavision-baseddetectionalgorithmforanunderwatergarbagecleanuprobot,whichisbasedontheenhancedYOLOv4(YouOnlyLookOnce)objectdetectionframework.Thealgorithmaimstoimprovetheaccuracyandefficiencyofgarbagedetectioninunderwaterenvironments.
1.Introduction
Theaccumulationofgarbageinaquaticenvironmentsadverselyaffectsmarinelife,ecosystems,andevenhumanactivitiessuchasfishingandtourism.Underwatergarbagecleanuprobotsofferapromisingsolutiontothisproblem.Efficientgarbagedetectionandcollectionarecriticalforthesuccessoftheserobots.Hence,thispaperintroducesanovelvision-baseddetectionalgorithmthatenhancesthecapabilitiesoftheunderwatergarbagecleanuprobot.
2.RelatedWork
PreviousstudieshaveutilizedcomputervisiontechniquessuchasConvolutionalNeuralNetworks(CNNs)forobjectdetectioninunderwaterenvironments.YOLOv4,areal-timeobjectdetectionframework,hasdemonstratedsuperiorperformanceinvariousapplications.ThispaperbuildsupontheYOLOv4frameworktoaddressthespecificchallengesofgarbagedetectionunderwater.
3.Methodology
3.1DataCollectionandPre-processing
Underwatergarbagedatasetcollectioninvolvescapturingimagesandvideosindifferentunderwatersettings.Thecollecteddataneedtobeannotatedwithlabelsindicatingthepresenceandlocationofgarbageobjects.Pre-processingtechniquessuchasimageandvideostabilization,noisereduction,andcolorcorrectionareappliedtoenhancethequalityofthedataset.
3.2EnhancedYOLOv4Architecture
TheYOLOv4architectureismodifiedtoadapttounderwatergarbagedetection.Thismodificationinvolvesintegratingadditionallayersintothenetworktoimproveitsabilitytodetectsmallandlo