RDD2022 - The multi-national Road Damage Dataset released through CRDDC'2022

dataset

posted on 2022-10-29, 10:53 authored by Deeksha AryaDeeksha Arya, Hiroya Maeda, Yoshihide Sekimoto, Hiroshi Omata, Sanjay Kumar Ghosh, Durga Toshniwal, Madhavendra Sharma, Van Vung Pham, Jingtao Zhong, Muneer Al-Hammadi, Mamoona Birkhez Shami, Du Nguyen, Hanglin Cheng, Jing Zhang, Alex Klein-Paste, Helge Mork, Frank Lindseth, Toshikazu Seto, Alexander Mraz, Takehiro Kashiyama

Description

The Road Damage Dataset, RDD2022, is released as a part of the Crowdsensing-based Road Damage Detection Challenge (CRDDC'2022), an IEEE BigData Cup.
It 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.

Usage

The annotated dataset is envisioned for developing deep learning-based methods to detect and classify road damage automatically.
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.).

For further details, please refer to the CRDDC'2022 resources.