Citation

Arya D, Maeda H, Ghosh SK, Toshniwal D, Sekimoto Y. Data Brief 2021; 36: 107133.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.dib.2021.107133

PMID

unavailable

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].

Language: en

Keywords

Structural health monitoring; Deep learning; Automatic road condition monitoring; Crack recognition; Data qualification; Image; Pavement surface condition assessment; Quantification; Road damage dataset; Road infrastructure; Smartphone-based road damage detection and classification