Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification

Detalhes bibliográficos
Autor(a) principal: Oliveira, Roberto Manuel Trindade
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/94057
Resumo: Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia
id RCAP_4439e9f6545106d27015361edec55814
oai_identifier_str oai:estudogeral.uc.pt:10316/94057
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual ClassificationDevelopment of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual ClassificationDeep LearningConvolutional Neural NetworksFine-Grained Visual ClassificationPattern RecognitionComputer VisionDeep LearningConvolutional Neural NetworksFine-Grained Visual ClassificationPattern RecognitionComputer VisionDissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e TecnologiaDeep Learning and Convolutional Neural Networks have been staples in solving challenges relatedto Image Processing, Computer Vision and Pattern Recognition. Since their breakthrough in 2012 thatno other method has come close, be it in overall results, consistency, but also computation capabilities,with the technology evolving and delivering more and more impressive results as framework developerspush the boundaries. As a consequence, we are seeing work in the domain of Deep Learning creepmore and more in our ever day lives, automating a variety of tasks.In this work, Deep Learning will be applied to try and automate one such barely noticed task: tollcollection. In Porugal, Brisa operates an automatic toll collection service, which despite their bestefforts, is still fragile and subject to fraud. It is then in their interest that toll collection becomesas precise and reliable as possible. With this in mind, this document explores technology relatedto Vehicle Recognition, and applies state-of-the-art methodologies that ultimately deliver a solutionthat performs with state-of-the-art competitive results. The methodologies here applied can easily bereplicated and should translate well to other critical aspects of social life, like medical imaging baseddiagnosing.This work was submitted for the partial fulfillment of the requirements to complete the Integrated Master in Electrical and Computer Engineering, Automation Specialization, on October 2020. The work was supervised by Professor Jorge Manuel Moreira de Campos Pereira Batista, Phd.Deep Learning and Convolutional Neural Networks have been staples in solving challenges relatedto Image Processing, Computer Vision and Pattern Recognition. Since their breakthrough in 2012 thatno other method has come close, be it in overall results, consistency, but also computation capabilities,with the technology evolving and delivering more and more impressive results as framework developerspush the boundaries. As a consequence, we are seeing work in the domain of Deep Learning creepmore and more in our ever day lives, automating a variety of tasks.In this work, Deep Learning will be applied to try and automate one such barely noticed task: tollcollection. In Porugal, Brisa operates an automatic toll collection service, which despite their bestefforts, is still fragile and subject to fraud. It is then in their interest that toll collection becomesas precise and reliable as possible. With this in mind, this document explores technology relatedto Vehicle Recognition, and applies state-of-the-art methodologies that ultimately deliver a solutionthat performs with state-of-the-art competitive results. The methodologies here applied can easily bereplicated and should translate well to other critical aspects of social life, like medical imaging baseddiagnosing.This work was submitted for the partial fulfillment of the requirements to complete the Integrated Master in Electrical and Computer Engineering, Automation Specialization, on October 2020. The work was supervised by Professor Jorge Manuel Moreira de Campos Pereira Batista, Phd.2020-11-13info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10316/94057http://hdl.handle.net/10316/94057TID:202686604engOliveira, Roberto Manuel Trindadeinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2021-05-25T07:34:38Zoai:estudogeral.uc.pt:10316/94057Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:12:53.323052Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification
Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification
title Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification
spellingShingle Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification
Oliveira, Roberto Manuel Trindade
Deep Learning
Convolutional Neural Networks
Fine-Grained Visual Classification
Pattern Recognition
Computer Vision
Deep Learning
Convolutional Neural Networks
Fine-Grained Visual Classification
Pattern Recognition
Computer Vision
title_short Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification
title_full Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification
title_fullStr Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification
title_full_unstemmed Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification
title_sort Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification
author Oliveira, Roberto Manuel Trindade
author_facet Oliveira, Roberto Manuel Trindade
author_role author
dc.contributor.author.fl_str_mv Oliveira, Roberto Manuel Trindade
dc.subject.por.fl_str_mv Deep Learning
Convolutional Neural Networks
Fine-Grained Visual Classification
Pattern Recognition
Computer Vision
Deep Learning
Convolutional Neural Networks
Fine-Grained Visual Classification
Pattern Recognition
Computer Vision
topic Deep Learning
Convolutional Neural Networks
Fine-Grained Visual Classification
Pattern Recognition
Computer Vision
Deep Learning
Convolutional Neural Networks
Fine-Grained Visual Classification
Pattern Recognition
Computer Vision
description Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia
publishDate 2020
dc.date.none.fl_str_mv 2020-11-13
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/94057
http://hdl.handle.net/10316/94057
TID:202686604
url http://hdl.handle.net/10316/94057
identifier_str_mv TID:202686604
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799134023889453056

Registros relacionados