UrOAC: Urban Objects in Any-light Conditions
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Título: | UrOAC: Urban Objects in Any-light Conditions |
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Autor/es: | Gomez-Donoso, Francisco | Moreno Martínez, Marcos | Cazorla, Miguel |
Grupo/s de investigación o GITE: | Robótica y Visión Tridimensional (RoViT) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Object recognition | Low-light conditions | Urban environments |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | 14-abr-2022 |
Editor: | Elsevier |
Cita bibliográfica: | Data in Brief. 2022, 42: 108172. https://doi.org/10.1016/j.dib.2022.108172 |
Resumen: | In the past years, several works on urban object detection from the point of view of a person have been made. These works are intended to provide an enhanced understanding of the environ- ment for blind and visually challenged people. The mentioned approaches mostly rely in deep learning and machine learning methods. Nonetheless, these approaches only work with direct and bright light, namely, they will only perform correctly on daylight conditions. This is because deep learning algorithms require large amounts of data and the currently available datasets do not address this matter. In this work, we propose UrOAC, a dataset of urban objects captured in a range of different lightning conditions, from bright daylight to low and poor night-time lighting conditions. In the latter, the objects are only lit by low ambient light, street lamps and headlights of passing-by vehicles. The dataset depicts the following objects: pedestrian crosswalks, green traffic lights and red traffic lights. The annotations include the category and the bounding-box of each object. This dataset could be used for improve the performance at night-time and under low-light conditions of any vision-based method that involves urban objects. For instance, guidance and object detection devices for the visually challenged or self-driving and intelligent vehicles. |
Patrocinador/es: | This work has been supported by the Spanish Government PID2019-104818RB-I00 Grant, co-funded by EU Structural Funds. |
URI: | http://hdl.handle.net/10045/123143 |
ISSN: | 2352-3409 |
DOI: | 10.1016/j.dib.2022.108172 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.dib.2022.108172 |
Aparece en las colecciones: | INV - RoViT - Artículos de Revistas |
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Gomez-Donoso_etal_2022_Data-in-Brief.pdf | 474,22 kB | Adobe PDF | Abrir Vista previa | |
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