dc.contributorSilva, Jean Carlos Cardozo da
dc.contributorhttp://lattes.cnpq.br/9949032159595994
dc.contributorMartelli, Cicero
dc.contributorhttp://lattes.cnpq.br/9188974272555318
dc.contributorSilva, Jean Carlos Cardozo da
dc.contributorBrito, Lélio Antônio Teixeira
dc.contributorSilva, Marco Jose da
dc.creatorDi Renzo, André Biffe
dc.date.accessioned2017-12-28T01:05:48Z
dc.date.accessioned2022-12-06T14:40:04Z
dc.date.available2017-12-28T01:05:48Z
dc.date.available2022-12-06T14:40:04Z
dc.date.created2017-12-28T01:05:48Z
dc.date.issued2017-10-20
dc.identifierDI RENZO, André Biffe. Desenvolvimento de metodologia para monitoramento remoto de rodovias: VANTRod. 2017. 96 f. Dissertação (Mestrado em Engenharia Elétrica e Informática Industrial) - Universidade Tecnológica Federal do Paraná, Curitiba, 2017.
dc.identifierhttp://repositorio.utfpr.edu.br/jspui/handle/1/2852
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5253626
dc.description.abstractHighways are the principal transportation modal way in Brazil for cargo and passengers. Highways can suffer wear due to weather and traffic load, hence necessary a constant monitoring of the pavement and traffic signalization health. In general, the highway health is monitored manually being necessary persons to make the verification process. One alternative to this process is the use of aerial images. This work presents a highway remote sensing methodology from aerial imagens and digital image processing (DIP), as a tool to verify the road conditions, including the pavement and road markings. The acquisition of images are performed by an Unmanned Aerial Vehicle (UAV) enabling large area scans with less time. With the acquired imagens, DIP techniques and pattern recognition are employed to extract and identify highways parameters. So, an algorithm was developed to process and extract road information of the acquired images providing inspection of highway with agility and precision. The developed algorithm has three parts: the first one make the roadway segmentation, the second segments objects of the road and the third classifies the segmented objects. To classify the segmented objects, the Histogram of Oriented Gradient (HOG) descriptor has been used to extract characteristics of the objects and the Support Vector Machine () was used to classify the objects. With this developed algorithm, positive results has reached in obtain road information from the aerial images. Performance tests has been performed and a hit rate of 97.37% was reached for the selected classes, proving the ability of this proposed methodology could be applied in real environment helping maintenance and management highway teams.
dc.publisherUniversidade Tecnológica Federal do Paraná
dc.publisherCuritiba
dc.publisherBrasil
dc.publisherPrograma de Pós-Graduação em Engenharia Elétrica e Informática Industrial
dc.publisherUTFPR
dc.rightsopenAccess
dc.subjectRodovias
dc.subjectProcessamento de sinais - Técnicas digitais
dc.subjectAlgorítmos computacionais
dc.subjectRodovias - Medidas de segurança
dc.subjectEngenharia elétrica
dc.subjectRoads
dc.subjectSignal processing - Digital technique
dc.subjectComputer algorithms
dc.titleDesenvolvimento de metodologia para monitoramento remoto de rodovias: VANTRod
dc.typemasterThesis


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