Dataset: Roundabout Aerial Images for Vehicle Detection.

This publication presents a dataset of Spanish roundabouts aerial images taken from a UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the...

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Detalles Bibliográficos
Autores: Puertas, Enrique, De las Heras, Gonzalo, Fernández-Andrés, Javier, Sánchez Soriano, Javier
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad Francisco de Vitoria
Repositorio:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
Idioma:inglés
OAI Identifier:oai:ddfv.ufv.es:10641/3286
Acceso en línea:https://hdl.handle.net/10641/3286
Access Level:acceso abierto
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oai_identifier_str oai:ddfv.ufv.es:10641/3286
network_acronym_str ES
network_name_str España
repository_id_str
spelling Dataset: Roundabout Aerial Images for Vehicle Detection.Puertas, EnriqueDe las Heras, GonzaloFernández-Andrés, JavierSánchez Soriano, JavierThis publication presents a dataset of Spanish roundabouts aerial images taken from a UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing, and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2262 trucks, 7008 buses, and 2208 empty roundabouts in 61,896 1920 × 1080 px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research into computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection20222022-01-0120222022-01-01journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10641/3286reponame:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoriainstname:Universidad Francisco de VitoriaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ddfv.ufv.es:10641/32862026-06-11T12:44:57Z
dc.title.none.fl_str_mv Dataset: Roundabout Aerial Images for Vehicle Detection.
title Dataset: Roundabout Aerial Images for Vehicle Detection.
spellingShingle Dataset: Roundabout Aerial Images for Vehicle Detection.
Puertas, Enrique
title_short Dataset: Roundabout Aerial Images for Vehicle Detection.
title_full Dataset: Roundabout Aerial Images for Vehicle Detection.
title_fullStr Dataset: Roundabout Aerial Images for Vehicle Detection.
title_full_unstemmed Dataset: Roundabout Aerial Images for Vehicle Detection.
title_sort Dataset: Roundabout Aerial Images for Vehicle Detection.
dc.creator.none.fl_str_mv Puertas, Enrique
De las Heras, Gonzalo
Fernández-Andrés, Javier
Sánchez Soriano, Javier
author Puertas, Enrique
author_facet Puertas, Enrique
De las Heras, Gonzalo
Fernández-Andrés, Javier
Sánchez Soriano, Javier
author_role author
author2 De las Heras, Gonzalo
Fernández-Andrés, Javier
Sánchez Soriano, Javier
author2_role author
author
author
dc.contributor.none.fl_str_mv
description This publication presents a dataset of Spanish roundabouts aerial images taken from a UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing, and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2262 trucks, 7008 buses, and 2208 empty roundabouts in 61,896 1920 × 1080 px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research into computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01
2022
2022-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10641/3286
url https://hdl.handle.net/10641/3286
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
instname:Universidad Francisco de Vitoria
instname_str Universidad Francisco de Vitoria
reponame_str DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
collection DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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