基于YOLOv3的改进仪表检测算法

基于YOLOv3的改进仪表检测算法Title:AModifiedYOLOv3-basedAlgorithmforInstrumentDetectionAbstract:Inrecentyears,objectdetectionhaswitnessedsubstantialprogressduetotheemergenceofdeeplearningtechniques.OneprominentobjectdetectionalgorithmisYouOnlyLookOnce(YOLO),whichhasgainedpopularityforitsreal-timeperformance.However,traditionalYOLOv3oftenfailstoachievesatisfactoryperformanceindetectingsmallobjectssuchasinstrumentsinvariousscenarios.Inthispaper,weproposeamodifiedYOLOv3-basedalgorithmforinstrumentdetection.Theproposedalgorithmaimstoimprovethedetectionaccuracyandefficiencywhilemaintainingreal-timeperformance.1.Introduction1.1BackgroundInstrumentdetectionplaysacriticalroleinvariousapplications,includinghealthcare,manufacturing,andautomation.Inthesescenarios,accuratelyidentifyingandlocalizinginstrumentsarenecessaryfordownstreamtasks.Therefore,thereisagrowingdemandforefficientandreliableinstrumentdetectionalgorithms.1.2MotivationDespitethesuccessofYOLOv3inobjectdetection,itoftenstruggleswiththedetectionofsmallobjects,suchasinstruments.Theseobjectstendtohavelowcontrast,intricateshapes,andsmallsizes,whichmakethemchallengingtodetectaccurately.Moreover,thereal-timeperformanceofYOLOv3canbecompromisedundersuchcircumstances.Hence,amodifiedversionofYOLOv3specificallydesignedforinstrumentdetectionisrequired.2.Methodology2.1YOLOv3OverviewAbriefexplanationoftheoriginalYOLOv3algorithm,includingitsarchitectureandkeycomponents,ispresentedinthissection.Thisservesasthefoundationfortheproposedmodifications.2.2ProposedModificationsToenhancetheinstrumentdetectioncapabilityofYOLOv3,weproposethefollowingmodifications:-FeaturePyramidNetwork(FPN):WeintegratetheFPNintotheYOLOv3architecturetoimprovetheabilitytodetectobjectsatdifferentscales.ThisaddressesthecommonissueofsmallinstrumentdetectioninYOLOv3.-AnchorOptimization:Weproposeanovelanchoroptimizationmethodtoadjusttheanchorscalesandaspectratiostobetteralignwiththecharacteristicsofinstrumentobjects.Thishelpscaptureinstrumentobjectswithgreateraccuracy.-DataAugmentation:Weintroducevariousdataaugmentationtechniques,suchasrotation,translation,andscalechanges,toincreasethediversityoftrainingdata.Thisfurtherimprovesthemodel'sgeneralizationabilityandrobustnesstodifferentinstrumenttypesandorientations.3.ExperimentalEvaluation3.1DatasetPreparationWecollectandannotateadatasetspecificallydesignedforinstrumentdetection.Thedatasetincludesvarioustypesofinstrumentswitharangeofsizes,orientations,andlightingconditions.Additionally,wesplitthedatasetintotraining,validation,andtestingsubsets.3.2ExperimentalSetupWeconductexperimentsonahigh-performancecomputingplatformequippedwithaGPUtoevaluatetheproposedalgorithm'sperformance.WecompareitwithboththeoriginalYOLOv3andotherstate-of-the-artinstrumentdetectionalgorithmstodemonstratetheeffectivenessofourmodifications.3.3PerformanceEvaluationWeusevariousevaluationmetricssuchasprecision,recall,andmeanAveragePrecision(mAP)toassesstheproposedalgorithm'sperformance.Theevaluationisperformedonthetestingsubsetofthedataset.4.ResultsandDiscussionWepresentanddiscusstheexperimentalresultsinthissection.Theperformancecomparisonbetweentheproposedalgorithmandexistingtechniquesunderscoresitssuperiorityininstrumentdetection,particularlyindetectingsmallandintricateinstrumentobjects.5.ConclusionsInthispaper,weproposedamodifiedYOLOv3-basedalgorithmforinstrumentdetection.Theproposedmodificationsaimedtoenhancethedetectionaccuracyandefficiencywhilemaintainingreal-timeperformance.ExperimentalresultsdemonstratedthatouralgorithmoutperformstheoriginalYOLOv3andotherstate-of-the-artalgorithmsininstrumentdetection,especiallyforsmallandintricateinstruments.ThemodifiedYOLOv3algorithmshowspromiseforpracticalapplicationsinhealthcare,manufacturing,andautomation.References:[List