Object detection with artificial vision and neural networks for service robots

Detalhes bibliográficos
Autor(a) principal: Pinto, Tiago Alexandre Barbosa
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/62251
Resumo: Dissertação de mestrado em Engenharia Eletrónica Industrial e Computadores
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spelling Object detection with artificial vision and neural networks for service robotsDeep learningComputer visionConvolutional neural networksObject detectionService robotTensorFlowEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e InformáticaDissertação de mestrado em Engenharia Eletrónica Industrial e ComputadoresThis dissertation arises from a major project that consists on developing a domestic service robot, named CHARMIE (Collaborative Home Assistant Robot by Minho Industrial Electronics), to cooperate and help on domestic tasks. In general, the project aims to implement artificial intelligence in the whole robot. The main contribution of this dissertation is the development of the vision system, with artificial intelligence, to classify and detect, in real time, the objects represented on the environment that the robot is placed. This dissertation is within two broad areas that revolutionized the robotics industry, namely the artificial vision and artificial intelligence. Knowing that most of the existent information is presented on the vision and with the evolution of robotics, there was a need to introduce the capacity to acquire and process this kind of information. So, the artificial vision algorithms allowed them to acquire information of the environment, namely patterns, objects, formats, through vision sensors (cameras). Although implementing artificial vision can be very complex if it is intended to detect objects, due to image complexity. The introduction of artificial intelligence, more precisely, deep learning, brought the capability of implementing systems that can learn from provided data, without the need of hard coding it, reducing slightly the complexity and the time consumption of implementing complex problems. For artificial vision problems, like this project, there is a deep neural network that is specialized in learning from three dimensional vectors, namely images, named Convolutional Neural Network (CNN). This network uses image data to learn patterns, edges, formats, and many more, that represents a certain object. This type of network is used to classify and detect the objects presented in the image provided by the camera and is implemented with the Tensorflow library. All the image acquisition from the camera is performed by the OpenCv library. At the end of the dissertation, a model that allows real-time detection of objects from camera images is provided.Lopes, GilUniversidade do MinhoPinto, Tiago Alexandre Barbosa20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/1822/62251eng202302024info: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:RCAAP2023-07-21T12:22:44ZPortal AgregadorONG
dc.title.none.fl_str_mv Object detection with artificial vision and neural networks for service robots
title Object detection with artificial vision and neural networks for service robots
spellingShingle Object detection with artificial vision and neural networks for service robots
Pinto, Tiago Alexandre Barbosa
Deep learning
Computer vision
Convolutional neural networks
Object detection
Service robot
TensorFlow
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
title_short Object detection with artificial vision and neural networks for service robots
title_full Object detection with artificial vision and neural networks for service robots
title_fullStr Object detection with artificial vision and neural networks for service robots
title_full_unstemmed Object detection with artificial vision and neural networks for service robots
title_sort Object detection with artificial vision and neural networks for service robots
author Pinto, Tiago Alexandre Barbosa
author_facet Pinto, Tiago Alexandre Barbosa
author_role author
dc.contributor.none.fl_str_mv Lopes, Gil
Universidade do Minho
dc.contributor.author.fl_str_mv Pinto, Tiago Alexandre Barbosa
dc.subject.por.fl_str_mv Deep learning
Computer vision
Convolutional neural networks
Object detection
Service robot
TensorFlow
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
topic Deep learning
Computer vision
Convolutional neural networks
Object detection
Service robot
TensorFlow
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
description Dissertação de mestrado em Engenharia Eletrónica Industrial e Computadores
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-01T00:00:00Z
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/1822/62251
url http://hdl.handle.net/1822/62251
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 202302024
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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
repository.mail.fl_str_mv
_version_ 1777303742869143552

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