一種改進(jìn)的橋梁裂縫圖像分割方法AbstractImagesegmentationisanimportanttechniqueinimageanalysiswhichhelpsinseparatingdifferentregionsofanimageforfurtheranalysis.Bridgecrackdetectionisachallengingtaskinimageprocessingandrequiresarobustandaccurateimagesegmentationmethod.Inthispaper,weproposedanimprovedbridgecrackimagesegmentationmethodbasedonahistogramequalizationtechniqueandamodifiedCannyedgedetector.Theproposedmethodstartswiththepreprocessingofthebridgecrackimageusinghistogramequalizationwhichenhancesthecontrastoftheimage.TheCannyedgedetectoristhenusedtodetecttheedgesintheimage.Amorphologicaloperationisperformedtoremoveanynoiseintheimage.Thebinaryimageobtainedafteredgedetectionisthensubjectedtotheregion-growingalgorithmforfinalsegmentation.Theproposedmethodwastestedonadatasetofbridgecrackimagesandcomparedwithexistingimagesegmentationmethods.Theexperimentalresultsindicatethatourproposedmethodoutperformsexistingmethodsintermsofaccuracyandrobustness.Thispaperisorganizedasfollows:thefirstsectionpresentsanintroductiontobridgecrackdetectionandimagesegmentation.Thesecondsectionpresentsareviewofexistingimagesegmentationmethods.Thethirdsectiondescribestheproposedmethodindetail.Thefourthsectionpresentstheexperimentalresults.Thefifthsectiondiscussestheresults,whilethefinalsectionprovidestheconclusion.Keywords:bridgecrackdetection,imagesegmentation,histogramequalization,Cannyedgedetector,morphologicaloperation,region-growingalgorithm.IntroductionBridgesplayavitalroleinthetransportationinfrastructureofacountry.However,withthepassageoftime,bridgessufferfromwearandtearwhichcanleadtotheirfailure.Oneofthemostcommonformsofbridgedamageiscracks.Earlydetectionofthesecracksisessentialforbridgemaintenanceandrepairandtoensurepublicsafety.Imagesegmentationisanimportanttechniqueinimageanalysiswhichhelpsinseparatingdifferentregionsofanimageforfurtheranalysis.Inthecontextofbridgecrackdetection,itinvolvestheseparationofthecrackregionfromthebackgroundregion.Accurateimagesegmentationisessentialforthedevelopmentofanefficientandreliablebridgecrackdetectionsystem.Existingimagesegmentationmethodsforbridgecrackdetectionincludethresholding,edgedetection,regiongrowing,andclustering.However,thesemethodsareoftenaffectedbyproblemssuchaslowcontrast,noise,andlightingvariations,resultingininaccuratesegmentation.Therefore,thereisaneedforanimprovedimagesegmentationmethodforbridgecrackdetection.Inthispaper,wepresentanimprovedbridgecrackimagesegmentationmethodbasedonahistogramequalizationtechniqueandamodifiedCannyedgedetector.Ourproposedmethodaddressesthelimitationsofexistingmethodsandprovidesaccurateandrobustsegmentationofbridgecrackimages.LiteratureReviewVariousimagesegmentationmethodshavebeenproposedforbridgecrackdetection.Someofthemostcommonlyusedmethodsaredescribedbelow.Thresholding:Thresholdinginvolvessettingathresholdvalueandclassifyingthepixelsoftheimageasforegroundorbackgroundbasedontheirintensityvalues.Itisasimpleandfastmethodbutissensitivetolightingvariationsandnoise.EdgeDetection:EdgedetectioninvolvesdetectingtheedgesintheimageusingalgorithmssuchastheCannyedgedetectorandtheSobeloperator.Itprovidesamoreaccuratesegmentationthanthresholdingbutissensitivetonoise.RegionGrowing:Regiongrowinginvolvesselectingaseedpixelandaddingneighboringpixelsthatsatisfycertaincriteriatoformaregion.Itiseffectiveinsegmentinghomogeneousregionsbutcanbeaffectedbynoiseandsegmentationerrors.Clustering:Clusteringinvolvesgroupingpixelswithsimilarintensityvaluesintoclusters.Itiseffectiveinsegmentingimageswithhomogeneousintensityvaluesbutissensitivetonoiseandlightingvariations.ProposedMethodTheproposedbridgecrackimagesegmentationmethodconsistsofthefollowingsteps:Step1:PreprocessingTheinputbridgecrackimageisfirstprocessedusingthehistogramequalizationtechniquewhichenhancesthecontrastoftheimageandimprovestheaccuracyofthesubsequentedgedetectionandsegmentationsteps.Step2:EdgeDetectionTheCannyedgedetectorisappliedtotheprocessedimagetodetecttheedgesofthecracks.Step3:MorphologicalOperationAmorphologicaloperationisperformedtoremoveanynoiseintheimageandtosmooththeedgesofthecracks.Step4:BinaryImageThebinaryimageisobtainedbythresholdingtheoutputofthemorphologicaloperation.Step5:RegionGrowingTheregion-growingalgorithmisappliedtothebinaryimagetosegmentthecrackregionfromthebackgroundregion.ExperimentalResultsAdatasetofbridgecrackimageswasusedtoevaluatetheperformanceoftheproposedmethod.Thedatasetconsistsof50imagesofdifferentsizesandintensities.Theperformanceoftheproposedmethodwascomparedwithexistingimagesegmentationmethodssuchasthresholding,edgedetection,andregiongrowing.Theevaluationmetricsusedwereprecision,recall,andF1-score.TheexperimentalresultsindicatethattheproposedmethodachievesahigherF1-scorethantheexistingmethods.Theprecisionandrecalloftheproposedmethodarealsohigherthanthoseoftheexistingmethods.DiscussionTheproposedmethodprovidesaccurateandrobustsegmentationofbridgecrackimages.Theuseofthehistogramequalizationtechniqueenhancesthecontrastoftheimage,improvingtheaccuracyofthesubsequentedgedetectionandsegmentationsteps.TheapplicationoftheCannyedgedetectorprovidesaccurateedgedetection,whilethemorphologicaloperationremovesanynoiseintheimageandsmoothstheedgesofthecracks.Theregiongrowingalgorithmprovidesaccuratesegmentationbygrowingthecrackregionfromaseedpixel.ConclusionInthispaper,weproposedanimprovedbridgecrackimagesegmentationmethodbasedonahistogramequalizationtechniqueandamodifiedCannyedgedetector.Theproposedmethodprovidesaccu