RDD2022 - The multi-national Road Damage Dataset released through CRDDC'2022
datasetposted on 29.10.2022, 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
- 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.
- 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.