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 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.

描述

《道路损伤数据集》，简称 RDD2022，作为基于众包的道路损伤检测挑战赛（CRDDC'2022）的一部分，由 IEEE BigData Cup 发布。该数据集包含来自六个国家，即日本、印度、捷克共和国、挪威、美国和中国，共计 47,420 张道路图像。

这些图像已被标注超过 55,000 个道路损伤实例。

数据集中捕捉了四种道路损伤类型，包括纵向裂缝、横向裂缝、鳄鱼裂纹和坑洞。

用途

该标注数据集旨在开发基于深度学习的自动检测和分类道路损伤的方法。

市政部门和道路管理机构可以利用 RDD2022 数据集，并使用 RDD2022 训练的模型进行低成本的道路状况自动监控。

此外，计算机视觉和机器学习研究人员可以使用该数据集来评估不同算法在同类图像应用（如分类、目标检测等）中的性能。

欲了解更多详细信息，请参阅 CRDDC'2022 资源。