Detection and Classification of Obstacles for Autonomous Vessels Using Machine Learning

Detalhes bibliográficos
Autor(a) principal: António Pedro Rodrigues Pereira
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/122034
Resumo: Development of a system capable of obstacle detection and classification of various types that may be subject of collisions and result in damages to the ship or even its own total loss. The system is also capable of detection the horizon line, to estimate the relative distance of the detected objects to the vehicle current position. This is achieved throught Deep Learning techniques, namely by the use of Convolutional Neural Networks.
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spelling Detection and Classification of Obstacles for Autonomous Vessels Using Machine LearningEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringDevelopment of a system capable of obstacle detection and classification of various types that may be subject of collisions and result in damages to the ship or even its own total loss. The system is also capable of detection the horizon line, to estimate the relative distance of the detected objects to the vehicle current position. This is achieved throught Deep Learning techniques, namely by the use of Convolutional Neural Networks.2019-07-182019-07-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/122034TID:202389910engAntónio Pedro Rodrigues Pereirainfo: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:RCAAP2023-11-29T15:53:25Zoai:repositorio-aberto.up.pt:10216/122034Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:34:39.107518Repositório 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 Detection and Classification of Obstacles for Autonomous Vessels Using Machine Learning
title Detection and Classification of Obstacles for Autonomous Vessels Using Machine Learning
spellingShingle Detection and Classification of Obstacles for Autonomous Vessels Using Machine Learning
António Pedro Rodrigues Pereira
Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
title_short Detection and Classification of Obstacles for Autonomous Vessels Using Machine Learning
title_full Detection and Classification of Obstacles for Autonomous Vessels Using Machine Learning
title_fullStr Detection and Classification of Obstacles for Autonomous Vessels Using Machine Learning
title_full_unstemmed Detection and Classification of Obstacles for Autonomous Vessels Using Machine Learning
title_sort Detection and Classification of Obstacles for Autonomous Vessels Using Machine Learning
author António Pedro Rodrigues Pereira
author_facet António Pedro Rodrigues Pereira
author_role author
dc.contributor.author.fl_str_mv António Pedro Rodrigues Pereira
dc.subject.por.fl_str_mv Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
topic Engenharia electrotécnica, electrónica e informática
Electrical engineering, Electronic engineering, Information engineering
description Development of a system capable of obstacle detection and classification of various types that may be subject of collisions and result in damages to the ship or even its own total loss. The system is also capable of detection the horizon line, to estimate the relative distance of the detected objects to the vehicle current position. This is achieved throught Deep Learning techniques, namely by the use of Convolutional Neural Networks.
publishDate 2019
dc.date.none.fl_str_mv 2019-07-18
2019-07-18T00:00:00Z
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 https://hdl.handle.net/10216/122034
TID:202389910
url https://hdl.handle.net/10216/122034
identifier_str_mv TID:202389910
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.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
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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
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