Cargando…

A road surface image dataset with detailed annotations for driving assistance applications

The preview of the road surface states is essential for improving the safety and the ride comfort of autonomous vehicles. The created dataset in this data article consists of 370151 road surface images captured under a wide range of road and weather conditions in China. The original pictures are acq...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhao, Tong, Wei, Yintao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343931/
https://www.ncbi.nlm.nih.gov/pubmed/35928344
http://dx.doi.org/10.1016/j.dib.2022.108483
_version_ 1784761099758862336
author Zhao, Tong
Wei, Yintao
author_facet Zhao, Tong
Wei, Yintao
author_sort Zhao, Tong
collection PubMed
description The preview of the road surface states is essential for improving the safety and the ride comfort of autonomous vehicles. The created dataset in this data article consists of 370151 road surface images captured under a wide range of road and weather conditions in China. The original pictures are acquired with a vehicle-mounted camera and then the patches containing only the road surface area are cropped. The friction level, material, and unevenness properties of each road image are annotated in detail. This large-scale dataset is useful for developing vision-based road sensing modules to improve the performance of the driving assistance systems. Also, deep-learning experts can regard this dataset as a comparing benchmark for their algorithms. The dataset is available at [1].
format Online
Article
Text
id pubmed-9343931
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-93439312022-08-03 A road surface image dataset with detailed annotations for driving assistance applications Zhao, Tong Wei, Yintao Data Brief Data Article The preview of the road surface states is essential for improving the safety and the ride comfort of autonomous vehicles. The created dataset in this data article consists of 370151 road surface images captured under a wide range of road and weather conditions in China. The original pictures are acquired with a vehicle-mounted camera and then the patches containing only the road surface area are cropped. The friction level, material, and unevenness properties of each road image are annotated in detail. This large-scale dataset is useful for developing vision-based road sensing modules to improve the performance of the driving assistance systems. Also, deep-learning experts can regard this dataset as a comparing benchmark for their algorithms. The dataset is available at [1]. Elsevier 2022-07-23 /pmc/articles/PMC9343931/ /pubmed/35928344 http://dx.doi.org/10.1016/j.dib.2022.108483 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Zhao, Tong
Wei, Yintao
A road surface image dataset with detailed annotations for driving assistance applications
title A road surface image dataset with detailed annotations for driving assistance applications
title_full A road surface image dataset with detailed annotations for driving assistance applications
title_fullStr A road surface image dataset with detailed annotations for driving assistance applications
title_full_unstemmed A road surface image dataset with detailed annotations for driving assistance applications
title_short A road surface image dataset with detailed annotations for driving assistance applications
title_sort road surface image dataset with detailed annotations for driving assistance applications
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343931/
https://www.ncbi.nlm.nih.gov/pubmed/35928344
http://dx.doi.org/10.1016/j.dib.2022.108483
work_keys_str_mv AT zhaotong aroadsurfaceimagedatasetwithdetailedannotationsfordrivingassistanceapplications
AT weiyintao aroadsurfaceimagedatasetwithdetailedannotationsfordrivingassistanceapplications
AT zhaotong roadsurfaceimagedatasetwithdetailedannotationsfordrivingassistanceapplications
AT weiyintao roadsurfaceimagedatasetwithdetailedannotationsfordrivingassistanceapplications