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The data article describes the Road Damage Dataset, RDD2022, which comprises47,420 road images from six countries, Japan, India, the Czech Republic,Norway, the United States, and China. The images have been annotated with morethan 55,000 instances of road damage. Four types of road damage, namelylongitudinal cracks, transverse cracks, alligator cracks, and potholes, arecaptured in the dataset. The annotated dataset is envisioned for developingdeep learning-based methods to detect and classify road damage automatically.The dataset has been released as a part of the Crowd sensing-based Road DamageDetection Challenge (CRDDC2022). The challenge CRDDC2022 invites researchersfrom across the globe to propose solutions for automatic road damage detectionin multiple countries. The municipalities and road agencies may utilize theRDD2022 dataset, and the models trained using RDD2022 for low-cost automaticmonitoring of road conditions. Further, computer vision and machine learningresearchers may use the dataset to benchmark the performance of differentalgorithms for other image-based applications of the same type (classification,object detection, etc.).

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