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

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 <strong>47,420 road images</strong> from six countries, <strong>Japan, India, the Czech Republic, Norway, the United States, and China. </strong>
The images have been annotated with more than <strong>55,000</strong> 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 <strong>deep learning</strong>-based methods to detect and classify road damage <strong>automatically. </strong>
The <strong>municipalities and road agencies </strong>may utilize the RDD2022 dataset, and the models trained using RDD2022 for low-cost automatic monitoring of road conditions. 
Further,<strong> computer vision and machine learning researchers </strong>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.

## 说明
道路损伤数据集RDD2022（Road Damage Dataset, RDD2022）作为IEEE大数据杯（IEEE BigData Cup）赛事的子项——基于众包感知的道路损伤检测挑战赛（CRDDC'2022）的组成部分正式发布。
该数据集包含来自6个国家的47420张道路图像，涵盖日本、印度、捷克共和国、挪威、美国及中国。
所有图像均已标注超过55000个道路损伤实例。
本数据集收录了四类道路损伤类型，分别为纵向裂缝、横向裂缝、龟裂及路面坑槽。

## 应用场景
该标注数据集旨在研发基于深度学习（deep learning）的道路损伤自动检测与分类方法。
市政部门与道路管理机构可利用RDD2022数据集，以及基于该数据集训练得到的模型，实现低成本的道路状况自动监测。
此外，计算机视觉与机器学习研究人员可借助该数据集，为同类图像应用（如分类、目标检测等）的不同算法性能开展基准测试。

如需了解更多细节，请参阅CRDDC'2022相关资源。