Departamento de Ensino Superior
URI permanente desta comunidadehttps://ri.ifam.edu.br/handle/4321/956
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Resultados da Pesquisa
Item Uso de redes neurais para classificação de resíduos sólidos(2024-11-26) Maciel, Kainy Medeiros; Santos, Alyson de Jesus dos; http://lattes.cnpq.br/5998752909180697; Santos, Alyson de Jesus dos; http://lattes.cnpq.br/5998752909180697; Rodrigues, Marlos André Silva Rodrigues; http://lattes.cnpq.br/0650250324042531; Nascimento, José Fábio de Lima; ttp://lattes.cnpq.br/0056522906128231; Alencar, Sérgio Costa Martins de; http://lattes.cnpq.br/7936103793279068Item Uso de redes neurais para identificação de embarcações(2024-09-23) Pedroso, Ytalo Ribeiro da Silva; Santos, Alyson de Jesus dos Santos; http://lattes.cnpq.br/5998752909180697; Santos, Alyson de Jesus dos; http://lattes.cnpq.br/5998752909180697; Souza, Daniel Fonseca de; http://lattes.cnpq.br/4043793492782488; Rodrigues, Marlos André Silva; http://lattes.cnpq.br/0650250324042531Due toitsuniquegeography,theriversoftheAmazonplayasignificant roleinthedailylife of theregion,includingbeingtheonlyaccessinmoreremotecommunities,andthelackof infrastructure inmuchofthiswaterwaynetworkmakesitconducivetothetransportofillegal goods duetothelackofsupervision. When comparedtourbanareas,itispossibletonoticethepotentialforpositiveassistanceto local authoritiesthroughtheuseofrecognitiontechnologies,butthelackofcommunication structures hinderstheimplementationofthesetechnologiesinthesamewayitisdoneincities. This projectproposesthedevelopmentofaneuralnetworkapplicationaimedatassistingin public securityissuesthroughthedetectionofvesselsinportregionslocatedintheStateof Amazonas, combiningArtificial IntelligencetechniquesandEmbeddedSystemsdevices,without the useofnetworkdatatransmission.Themethodologyconsistsof:(a)Acquisitionofvessel images atMarinadoDaviandtheportofManausModerna-StateofAmazonas;(b)Preparation of thedatasetintheexpectedformatforthemodel;(c)Installation,configuration, anduseofthe Jetson NanoBoardintrainingtheNeuralNetwork;(d)UseofTransferLearning,leveraginga pre-trained model(YOLOv4Tiny)formodelgenerationonthenewdataset;(e)Validationofthe obtained results. The resultsshowthatwiththeuseofversionsoptimizedforembeddeddevices,itwaspossible to executetheYOLOv4Tinyalgorithm,performingtheclassification ofvesselsandcontributing with newvesselimages,whichprovidegreaterregionalitytothepresenteddata.