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

This data article provides details for the RDD2020 dataset comprising 26,336 road images from India, Japan, and the Czech Republic with more than 31,0 00 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 bench-marking 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]. (C) 2021 The Authors. Published by Elsevier Inc.

Automated summary

The RDD2020 dataset includes road images from India, Japan, and the Czech Republic, totaling 26,336, capturing 4 types of road damage, aiming to develop deep learning methods to automatically detect and classify road damage, which can be used for developing low-cost methods for monitoring road pavement conditions.