RDD2022: A multi-national image dataset for automatic Road Damage Detection

TL;DR

The paper introduces RDD2022, a comprehensive multi-national image dataset with over 47,000 annotated road images for developing and benchmarking deep learning methods for automatic road damage detection across various countries.

Contribution

It provides a large, annotated dataset from multiple countries specifically designed for training and benchmarking road damage detection algorithms.

Findings

Dataset includes 47,420 images with 55,000 damage instances.

Enables development of deep learning models for multi-country road damage detection.

Supports benchmarking of computer vision algorithms for damage classification.

Abstract

The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated with more than 55,000 instances of road damage. Four types of road damage, namely longitudinal cracks, transverse cracks, alligator cracks, and potholes, are captured in the dataset. The annotated dataset is envisioned for developing deep learning-based methods to detect and classify road damage automatically. The dataset has been released as a part of the Crowd sensing-based Road Damage Detection Challenge (CRDDC2022). The challenge CRDDC2022 invites researchers from across the globe to propose solutions for automatic road damage detection in multiple countries. The municipalities and road agencies may utilize the RDD2022 dataset, and the models trained using RDD2022 for low-cost automatic monitoring of road conditions. Further, computer vision and machine learning researchers may use the dataset to benchmark the performance of different algorithms for other image-based applications of the same type (classification, object detection, etc.).