Reconhecimento e análise de rachaduras a partir de imagens para monitoramento em regiões com atividade sísmica frequente - Lima, João Miguel Correia (2019)
In riskareas,especiallythosecausedbyfrequentseismicmovements,itisnecessarytomonitor eventssuchaschangesinthesizeofcracksinbuildingsandotherurbanstructures.This process, especiallyinregionswithoutadequateinfrastructure,iscarriedoutthroughempirical observationsmadebytheoccupantsoftheseareasthemselves.Thisactivity,byitsverynature, results ininaccurateandoutdateddatacollection,giventhetimelapsebetweencollectionand measurement ofchanges,whichshouldbedonebyaspecialist.Inthispaper,wepropose algorithms basedoncomputervision,toautomatethisprocessbyemployingDeepLearning techniques. Thus,itispossibleforthecomputertodetectcracksandproperlymeasurecracksin images senttoresponsibleagenciesbyresidents,providingagilityindetectingpotentialrisks to people’sphysicalintegrity,whileprovidingexpertswithaccuratedataforactioneffective preventive.Wewilluseasthesituationexperiencedbyresidentsofsomeneighborhoodsinthe city ofMaceió,stateofAlagoas,Brazil,whererulersarecurrentlyemployedtoidentifythe advanceorindentationsofcracksinrealestate,whicharephotographeddailyandtheimages sent tosecurityagencies.Inthepresentworkwepresentaprocessdividedintothreesteps:The first istheidentificationofpointsofinterestintheruler,suchasdigits,usingtheYOLO,adeep neural networkarchitecture,thenwepresentanalgorithmtofilterthecorrectdigitsandfinallyto identify thecrackanditswidthbyapplyingimageprocessingalgorithmandcameracalibration. Thus, aftertheexperiments,wewereabletoachieve75,65%accuracywhenusingthedarktext implementation with31convolutionallayers.Therefore,thiswork,besidesbeinglifesaving,is also alowcosttoolforstructuralinspection. Keywords: