A framework for representing, building and reusing novel atate-of-the-art three-dimensional object detection models in point clouds targeting self-driving applications
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/86322 |
Resumo: | The rapid development of deep learning has brought novel methodologies for 3D object detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, technologies and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it has become crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow for the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow for a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements. |
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A framework for representing, building and reusing novel atate-of-the-art three-dimensional object detection models in point clouds targeting self-driving applicationsAutonomous drivingDeep learning methodsLiDAR sensing technology3D object detectionCiências Naturais::Ciências da Computação e da InformaçãoThe rapid development of deep learning has brought novel methodologies for 3D object detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, technologies and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it has become crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow for the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow for a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements.This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and the project “Integrated and Innovative Solutions for the well-being of people in complex urban centers” within the Project Scope NORTE-01-0145-FEDER 000086. The work of Pedro Oliveira was supported by the doctoral Grant PRT/BD/154311/2022 financed by the Portuguese Foundation for Science and Technology (FCT), and with funds from European Union, under MIT Portugal Program. The work of Paulo Novais and Dalila Durães is supported by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project 2022.06822.PTDC.Multidisciplinary Digital Publishing InstituteUniversidade do MinhoSilva, António José LinharesOliveira, PedroDurães, DalilaFernandes, DuarteNévoa, RafaelMonteiro, João L.Melo-Pinto, PedroMachado, José ManuelNovais, Paulo2023-07-152023-07-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/86322engSilva, A.L.; Oliveira, P.; Durães, D.; Fernandes, D.; Névoa, R.; Monteiro, J.; Melo-Pinto, P.; Machado, J.; Novais, P. A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications. Sensors 2023, 23, 6427. https://doi.org/10.3390/s231464271424-82201424-822010.3390/s2314642737514724https://www.mdpi.com/1424-8220/23/14/6427info: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:RCAAP2023-12-23T01:38:18Zoai:repositorium.sdum.uminho.pt:1822/86322Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:29:19.465866Repositório 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 |
A framework for representing, building and reusing novel atate-of-the-art three-dimensional object detection models in point clouds targeting self-driving applications |
title |
A framework for representing, building and reusing novel atate-of-the-art three-dimensional object detection models in point clouds targeting self-driving applications |
spellingShingle |
A framework for representing, building and reusing novel atate-of-the-art three-dimensional object detection models in point clouds targeting self-driving applications Silva, António José Linhares Autonomous driving Deep learning methods LiDAR sensing technology 3D object detection Ciências Naturais::Ciências da Computação e da Informação |
title_short |
A framework for representing, building and reusing novel atate-of-the-art three-dimensional object detection models in point clouds targeting self-driving applications |
title_full |
A framework for representing, building and reusing novel atate-of-the-art three-dimensional object detection models in point clouds targeting self-driving applications |
title_fullStr |
A framework for representing, building and reusing novel atate-of-the-art three-dimensional object detection models in point clouds targeting self-driving applications |
title_full_unstemmed |
A framework for representing, building and reusing novel atate-of-the-art three-dimensional object detection models in point clouds targeting self-driving applications |
title_sort |
A framework for representing, building and reusing novel atate-of-the-art three-dimensional object detection models in point clouds targeting self-driving applications |
author |
Silva, António José Linhares |
author_facet |
Silva, António José Linhares Oliveira, Pedro Durães, Dalila Fernandes, Duarte Névoa, Rafael Monteiro, João L. Melo-Pinto, Pedro Machado, José Manuel Novais, Paulo |
author_role |
author |
author2 |
Oliveira, Pedro Durães, Dalila Fernandes, Duarte Névoa, Rafael Monteiro, João L. Melo-Pinto, Pedro Machado, José Manuel Novais, Paulo |
author2_role |
author author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Silva, António José Linhares Oliveira, Pedro Durães, Dalila Fernandes, Duarte Névoa, Rafael Monteiro, João L. Melo-Pinto, Pedro Machado, José Manuel Novais, Paulo |
dc.subject.por.fl_str_mv |
Autonomous driving Deep learning methods LiDAR sensing technology 3D object detection Ciências Naturais::Ciências da Computação e da Informação |
topic |
Autonomous driving Deep learning methods LiDAR sensing technology 3D object detection Ciências Naturais::Ciências da Computação e da Informação |
description |
The rapid development of deep learning has brought novel methodologies for 3D object detection using LiDAR sensing technology. These improvements in precision and inference speed performances lead to notable high performance and real-time inference, which is especially important for self-driving purposes. However, the developments carried by these approaches overwhelm the research process in this area since new methods, technologies and software versions lead to different project necessities, specifications and requirements. Moreover, the improvements brought by the new methods may be due to improvements in newer versions of deep learning frameworks and not just the novelty and innovation of the model architecture. Thus, it has become crucial to create a framework with the same software versions, specifications and requirements that accommodate all these methodologies and allow for the easy introduction of new methods and models. A framework is proposed that abstracts the implementation, reusing and building of novel methods and models. The main idea is to facilitate the representation of state-of-the-art (SoA) approaches and simultaneously encourage the implementation of new approaches by reusing, improving and innovating modules in the proposed framework, which has the same software specifications to allow for a fair comparison. This makes it possible to determine if the key innovation approach outperforms the current SoA by comparing models in a framework with the same software specifications and requirements. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-15 2023-07-15T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/86322 |
url |
https://hdl.handle.net/1822/86322 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Silva, A.L.; Oliveira, P.; Durães, D.; Fernandes, D.; Névoa, R.; Monteiro, J.; Melo-Pinto, P.; Machado, J.; Novais, P. A Framework for Representing, Building and Reusing Novel State-of-the-Art Three-Dimensional Object Detection Models in Point Clouds Targeting Self-Driving Applications. Sensors 2023, 23, 6427. https://doi.org/10.3390/s23146427 1424-8220 1424-8220 10.3390/s23146427 37514724 https://www.mdpi.com/1424-8220/23/14/6427 |
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.publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute |
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 |
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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) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799133560982994944 |