Open Access

Data in Brief

, volume 36 , pages 107133

RDD2020: An annotated image dataset for automatic road damage detection using deep learning

Deeksha Arya ^{1, 2}

Hiroya MAEDA ³

S. Ghosh ⁴

Durga Toshniwal ⁵

Yoshihide Sekimoto ³

Hide authors affiliations Show authors affiliations: 5 affiliations

Center for Transportation Systems, Indian Institute of Technology Roorkee, 247667, India |

Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Tokyo, Japan |

Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Tokyo, Japan. |

⁴

Department of Civil Engineering, Indian Institute of Technology Roorkee, 247667, India |

⁵

Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, 247667, India |

Publication type: Journal Article

Publication date: 2021-06-01

Elsevier

Data in Brief

scimago Q3

wos Q3

SJR: 0.198

CiteScore: 2.6

Impact factor: 1.4

ISSN: 23523409

DOI: 10.1016/j.dib.2021.107133

Copy DOI

PubMed ID: 34095382

Multidisciplinary

Abstract

This data article provides details for the RDD2020 dataset comprising 26,336 road images from India, Japan, and the Czech Republic with more than 31,000 instances of road damage. The dataset captures four types of road damage: longitudinal cracks, transverse cracks, alligator cracks, and potholes; and is intended for developing deep learning-based methods to detect and classify road damage automatically. The images in RDD2020 were captured using vehicle-mounted smartphones, making it useful for municipalities and road agencies to develop methods for low-cost monitoring of road pavement surface conditions. Further, the machine learning researchers can use the datasets for benchmarking the performance of different algorithms for solving other problems of the same type (image classification, object detection, etc.). RDD2020 is freely available at [1] . The latest updates and the corresponding articles related to the dataset can be accessed at [2] .

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Cite this

GOST |

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GOST Copy

Arya D. et al. RDD2020: An annotated image dataset for automatic road damage detection using deep learning // Data in Brief. 2021. Vol. 36. p. 107133.

GOST all authors (up to 50) Copy

Arya D., MAEDA H., Ghosh S., Toshniwal D., Sekimoto Y. RDD2020: An annotated image dataset for automatic road damage detection using deep learning // Data in Brief. 2021. Vol. 36. p. 107133.

RIS |

Cite this

RIS Copy

TY - JOUR

DO - 10.1016/j.dib.2021.107133

UR - https://doi.org/10.1016/j.dib.2021.107133

TI - RDD2020: An annotated image dataset for automatic road damage detection using deep learning

T2 - Data in Brief

AU - Arya, Deeksha

AU - MAEDA, Hiroya

AU - Ghosh, S.

AU - Toshniwal, Durga

AU - Sekimoto, Yoshihide

PY - 2021

DA - 2021/06/01

PB - Elsevier

SP - 107133

VL - 36

PMID - 34095382

SN - 2352-3409

ER -

BibTex

Cite this

BibTex (up to 50 authors) Copy

@article{2021_Arya,

author = {Deeksha Arya and Hiroya MAEDA and S. Ghosh and Durga Toshniwal and Yoshihide Sekimoto},

title = {RDD2020: An annotated image dataset for automatic road damage detection using deep learning},

journal = {Data in Brief},

year = {2021},

volume = {36},

publisher = {Elsevier},

month = {jun},

url = {https://doi.org/10.1016/j.dib.2021.107133},

pages = {107133},

doi = {10.1016/j.dib.2021.107133}

}

Publisher

Elsevier

Journal

Data in Brief

scimago Q3

wos Q3

SJR

0.198

CiteScore

2.6

Impact factor

1.4

ISSN

23523409 (Print)