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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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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 |
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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 |
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