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

Deeksha Arya; Hiroya Maeda; Sanjay Kumar Ghosh; Durga Toshniwal; Yoshihide Sekimoto

doi:10.1016/j.dib.2021.107133

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

Data Brief. 2021 May 12:36:107133. doi: 10.1016/j.dib.2021.107133. eCollection 2021 Jun.

Authors

Deeksha Arya^{1

2}, Hiroya Maeda², Sanjay Kumar Ghosh^{1

3}, Durga Toshniwal^{1

4}, Yoshihide Sekimoto²

Affiliations

¹ Center for Transportation Systems, Indian Institute of Technology Roorkee, 247667, India.
² Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Tokyo, Japan.
³ Department of Civil Engineering, Indian Institute of Technology Roorkee, 247667, India.
⁴ Department of Computer Science & Engineering, Indian Institute of Technology Roorkee, 247667, India.

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].

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