Detection of traffic accidents using artificial intelligence

Detalhes bibliográficos
Autor(a) principal: Sánchez, Jesus Gerardo Ávila
Data de Publicação: 2024
Outros Autores: Monteagudo, Francisco Eneldo López, Ruiz, Francisco Javier Martinez, Rodríguez, Leticia del Carmen Ríos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: ITEGAM-JETIA
Texto Completo: https://itegam-jetia.org/journal/index.php/jetia/article/view/1109
Resumo: This article analyzes different architectures with which a neural network can be developed using computer vision with the objective of detecting traffic accidents. For the development of the software, the Java Script programming language was used, reaching the conclusion that the best architecture to use is a Convolutional Neural Network since it has the capabilities of detecting features within the images. At the same time, a database was developed with the necessary characteristics for the functioning of the neural network.
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spelling Detection of traffic accidents using artificial intelligenceDetección de accidentes de tráfico mediante inteligencia artificialDetecção de acidentes de trânsito usando inteligência artificialThis article analyzes different architectures with which a neural network can be developed using computer vision with the objective of detecting traffic accidents. For the development of the software, the Java Script programming language was used, reaching the conclusion that the best architecture to use is a Convolutional Neural Network since it has the capabilities of detecting features within the images. At the same time, a database was developed with the necessary characteristics for the functioning of the neural network.Este artículo analiza diferentes arquitecturas con las que se puede desarrollar una red neuronal mediante visión por ordenador con el objetivo de detectar accidentes de tráfico. Para el desarrollo del software se utilizó el lenguaje de programación Java Script, llegando a la conclusión que la mejor arquitectura a utilizar es una Red Neural Convolucional ya que tiene la capacidad de detectar características dentro de las imágenes. Paralelamente se desarrolló una base de datos con las características necesarias para el funcionamiento de la red neuronal.Este artigo analisa diferentes arquiteturas com as quais uma rede neural pode ser desenvolvida utilizando visão computacional com o objetivo de detectar acidentes de trânsito. Para o desenvolvimento do software foi utilizada a linguagem de programação Java Script, chegando-se à conclusão de que a melhor arquitetura a ser utilizada é uma Rede Neural Convolucional, pois possui capacidade de detectar características dentro das imagens. Paralelamente, foi desenvolvida uma base de dados com as características necessárias ao funcionamento da rede neural.ITEGAM - Instituto de Tecnologia e Educação Galileo da Amazônia2024-04-30info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfhttps://itegam-jetia.org/journal/index.php/jetia/article/view/110910.5935/jetia.v10i46.1109ITEGAM-JETIA; v.10 n.46 2024; 15-21ITEGAM-JETIA; v.10 n.46 2024; 15-21ITEGAM-JETIA; v.10 n.46 2024; 15-212447-022810.5935/jetia.v10i46reponame:ITEGAM-JETIAinstname:Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)instacron:ITEGAMenghttps://itegam-jetia.org/journal/index.php/jetia/article/view/1109/599Copyright (c) 2024 ITEGAM-JETIAinfo:eu-repo/semantics/openAccessSánchez, Jesus Gerardo ÁvilaMonteagudo, Francisco Eneldo LópezRuiz, Francisco Javier MartinezRodríguez, Leticia del Carmen Ríos2024-04-30T16:14:45Zoai:ojs.itegam-jetia.org:article/1109Revistahttps://itegam-jetia.org/journal/index.php/jetiaPRIhttps://itegam-jetia.org/journal/index.php/jetia/oaieditor@itegam-jetia.orgopendoar:2024-04-30T16:14:45ITEGAM-JETIA - Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)false
dc.title.none.fl_str_mv Detection of traffic accidents using artificial intelligence
Detección de accidentes de tráfico mediante inteligencia artificial
Detecção de acidentes de trânsito usando inteligência artificial
title Detection of traffic accidents using artificial intelligence
spellingShingle Detection of traffic accidents using artificial intelligence
Sánchez, Jesus Gerardo Ávila
title_short Detection of traffic accidents using artificial intelligence
title_full Detection of traffic accidents using artificial intelligence
title_fullStr Detection of traffic accidents using artificial intelligence
title_full_unstemmed Detection of traffic accidents using artificial intelligence
title_sort Detection of traffic accidents using artificial intelligence
author Sánchez, Jesus Gerardo Ávila
author_facet Sánchez, Jesus Gerardo Ávila
Monteagudo, Francisco Eneldo López
Ruiz, Francisco Javier Martinez
Rodríguez, Leticia del Carmen Ríos
author_role author
author2 Monteagudo, Francisco Eneldo López
Ruiz, Francisco Javier Martinez
Rodríguez, Leticia del Carmen Ríos
author2_role author
author
author
dc.contributor.author.fl_str_mv Sánchez, Jesus Gerardo Ávila
Monteagudo, Francisco Eneldo López
Ruiz, Francisco Javier Martinez
Rodríguez, Leticia del Carmen Ríos
description This article analyzes different architectures with which a neural network can be developed using computer vision with the objective of detecting traffic accidents. For the development of the software, the Java Script programming language was used, reaching the conclusion that the best architecture to use is a Convolutional Neural Network since it has the capabilities of detecting features within the images. At the same time, a database was developed with the necessary characteristics for the functioning of the neural network.
publishDate 2024
dc.date.none.fl_str_mv 2024-04-30
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://itegam-jetia.org/journal/index.php/jetia/article/view/1109
10.5935/jetia.v10i46.1109
url https://itegam-jetia.org/journal/index.php/jetia/article/view/1109
identifier_str_mv 10.5935/jetia.v10i46.1109
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://itegam-jetia.org/journal/index.php/jetia/article/view/1109/599
dc.rights.driver.fl_str_mv Copyright (c) 2024 ITEGAM-JETIA
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2024 ITEGAM-JETIA
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv ITEGAM - Instituto de Tecnologia e Educação Galileo da Amazônia
publisher.none.fl_str_mv ITEGAM - Instituto de Tecnologia e Educação Galileo da Amazônia
dc.source.none.fl_str_mv ITEGAM-JETIA; v.10 n.46 2024; 15-21
ITEGAM-JETIA; v.10 n.46 2024; 15-21
ITEGAM-JETIA; v.10 n.46 2024; 15-21
2447-0228
10.5935/jetia.v10i46
reponame:ITEGAM-JETIA
instname:Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)
instacron:ITEGAM
instname_str Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)
instacron_str ITEGAM
institution ITEGAM
reponame_str ITEGAM-JETIA
collection ITEGAM-JETIA
repository.name.fl_str_mv ITEGAM-JETIA - Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)
repository.mail.fl_str_mv editor@itegam-jetia.org
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