Automotive Interior Sensing - Imaging Solutions
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/10216/114061 |
Resumo: | This dissertation addresses the problem of object detection inside the vehicle. Comparing all the state-of-the-art algorithms, approaches based on R-CNN stand out in terms of Average Precision. Typically two-stage detectors have higher accuracy rates while one-stage detectors reaches lower inference times. Mask R-CNN was chosen thanks to the high values obtained for Average Precision without compromising inference times, as well as providing object instance segmentation. This may be useful for approaches such as Multiview in which it is important to match points of the image acquired from one camera with points of the image acquired by other camera in another position. It was necessary to test the adaptability of Mask R-CNN to other datasets by changing COCO dataset to have different number of classes. At the end, the trained network over Bosch dataset, was Faster R-CNN with Mask weights pre-trained over COCO. |
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7160 |
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Automotive Interior Sensing - Imaging SolutionsEngenharia electrotécnica, electrónica e informáticaElectrical engineering, Electronic engineering, Information engineeringThis dissertation addresses the problem of object detection inside the vehicle. Comparing all the state-of-the-art algorithms, approaches based on R-CNN stand out in terms of Average Precision. Typically two-stage detectors have higher accuracy rates while one-stage detectors reaches lower inference times. Mask R-CNN was chosen thanks to the high values obtained for Average Precision without compromising inference times, as well as providing object instance segmentation. This may be useful for approaches such as Multiview in which it is important to match points of the image acquired from one camera with points of the image acquired by other camera in another position. It was necessary to test the adaptability of Mask R-CNN to other datasets by changing COCO dataset to have different number of classes. At the end, the trained network over Bosch dataset, was Faster R-CNN with Mask weights pre-trained over COCO.2018-07-202018-07-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/114061TID:202114856engGuilherme Oliveira Santosinfo: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:RCAAP2023-11-29T16:10:14Zoai:repositorio-aberto.up.pt:10216/114061Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:38:28.176683Repositório 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 |
Automotive Interior Sensing - Imaging Solutions |
title |
Automotive Interior Sensing - Imaging Solutions |
spellingShingle |
Automotive Interior Sensing - Imaging Solutions Guilherme Oliveira Santos Engenharia electrotécnica, electrónica e informática Electrical engineering, Electronic engineering, Information engineering |
title_short |
Automotive Interior Sensing - Imaging Solutions |
title_full |
Automotive Interior Sensing - Imaging Solutions |
title_fullStr |
Automotive Interior Sensing - Imaging Solutions |
title_full_unstemmed |
Automotive Interior Sensing - Imaging Solutions |
title_sort |
Automotive Interior Sensing - Imaging Solutions |
author |
Guilherme Oliveira Santos |
author_facet |
Guilherme Oliveira Santos |
author_role |
author |
dc.contributor.author.fl_str_mv |
Guilherme Oliveira Santos |
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 |
This dissertation addresses the problem of object detection inside the vehicle. Comparing all the state-of-the-art algorithms, approaches based on R-CNN stand out in terms of Average Precision. Typically two-stage detectors have higher accuracy rates while one-stage detectors reaches lower inference times. Mask R-CNN was chosen thanks to the high values obtained for Average Precision without compromising inference times, as well as providing object instance segmentation. This may be useful for approaches such as Multiview in which it is important to match points of the image acquired from one camera with points of the image acquired by other camera in another position. It was necessary to test the adaptability of Mask R-CNN to other datasets by changing COCO dataset to have different number of classes. At the end, the trained network over Bosch dataset, was Faster R-CNN with Mask weights pre-trained over COCO. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-07-20 2018-07-20T00:00:00Z |
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/10216/114061 TID:202114856 |
url |
https://hdl.handle.net/10216/114061 |
identifier_str_mv |
TID:202114856 |
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 |
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 |
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1799136291576610816 |