Utilize este identificador para referenciar este registo:
https://hdl.handle.net/10316/94057
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.advisor | Batista, Jorge Manuel Moreira de Campos Pereira | - |
dc.contributor.author | Oliveira, Roberto Manuel Trindade | - |
dc.date.accessioned | 2021-03-29T22:23:27Z | - |
dc.date.available | 2021-03-29T22:23:27Z | - |
dc.date.issued | 2020-11-13 | - |
dc.date.submitted | 2021-03-29 | - |
dc.identifier.uri | https://hdl.handle.net/10316/94057 | - |
dc.description | Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia | - |
dc.description.abstract | 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. | por |
dc.description.abstract | 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. | eng |
dc.language.iso | eng | - |
dc.rights | openAccess | - |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Deep Learning | por |
dc.subject | Convolutional Neural Networks | por |
dc.subject | Fine-Grained Visual Classification | por |
dc.subject | Pattern Recognition | por |
dc.subject | Computer Vision | por |
dc.subject | Deep Learning | eng |
dc.subject | Convolutional Neural Networks | eng |
dc.subject | Fine-Grained Visual Classification | eng |
dc.subject | Pattern Recognition | eng |
dc.subject | Computer Vision | eng |
dc.title | Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification | eng |
dc.title.alternative | Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification | por |
dc.type | masterThesis | - |
degois.publication.location | DEEC | - |
degois.publication.title | Development of a Highway Tolling and Enforcement System Using Convolutional Neural Networks and Fine-Grained Visual Classification | eng |
dc.peerreviewed | yes | - |
dc.identifier.tid | 202686604 | - |
thesis.degree.discipline | Engenharia Electrotécnica e de Computadores | - |
thesis.degree.grantor | Universidade de Coimbra | - |
thesis.degree.level | 1 | - |
thesis.degree.name | Mestrado Integrado em Engenharia Electrotécnica e de Computadores | - |
uc.degree.grantorUnit | Faculdade de Ciências e Tecnologia - Departamento de Eng. Electrotécnica e de Computadores | - |
uc.degree.grantorID | 0500 | - |
uc.contributor.author | Oliveira, Roberto Manuel Trindade::0000-0001-9386-5203 | - |
uc.degree.classification | 18 | - |
uc.degree.presidentejuri | Barreto, João Pedro de Almeida | - |
uc.degree.elementojuri | Batista, Jorge Manuel Moreira de Campos Pereira | - |
uc.degree.elementojuri | Menezes, Paulo Jorge Carvalho | - |
uc.contributor.advisor | Batista, Jorge Manuel Moreira de Campos Pereira | - |
item.fulltext | Com Texto completo | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairetype | masterThesis | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.advisor.researchunit | ISR - Institute of Systems and Robotics | - |
crisitem.advisor.parentresearchunit | University of Coimbra | - |
crisitem.advisor.orcid | 0000-0003-2387-5961 | - |
Aparece nas coleções: | UC - Dissertações de Mestrado |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
Dissertacao_Roberto_Oliveira_v2_final-1.pdf | 14.94 MB | Adobe PDF | Ver/Abrir |
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