RDD2022: A multi-national image dataset for automatic Road Damage Detection
RDD2022: A multi-national image dataset for automatic Road Damage Detection
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DOI：
10.48550/arxiv.2209.08538
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发表时间：
2022-09
期刊：
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通讯作者：
Deeksha M. Arya;Hiroya Maeda;S. Ghosh;Durga Toshniwal;Yoshihide Sekimoto Indian Institute of Technology Roorkee-Yoshihide-Sekimoto-Indian-Institute-of-Technology-1917274719;India;T. U. O. Tokyo;Japan.;UrbanX Technologies;Inc.;Tokyo
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文献类型：
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作者：
Deeksha M. Arya;Hiroya Maeda;S. Ghosh;Durga Toshniwal;Yoshihide Sekimoto Indian Institute of Technology Roorkee-Yoshihide-Sekimoto-Indian-Institute-of-Technology-1917274719;India;T. U. O. Tokyo;Japan.;UrbanX Technologies;Inc.;Tokyo
这篇数据文章介绍了道路损害数据集RDD2022，该数据集包括来自六个国家（日本、印度、捷克共和国、挪威、美国和中国）的47,420张道路图像。这些图像已经标注了超过55,000个道路损坏的实例。数据集中捕获了四种类型的道路损伤，即纵向裂缝、横向裂缝、鳄鱼裂缝和坑洼。该注释数据集被设想用于开发基于深度学习的方法来自动检测和分类道路损伤。该数据集已作为基于人群感知的道路损伤检测挑战（CRDDC2022）的一部分发布。CRDDC2022挑战赛邀请来自世界各地的研究人员为多个国家的道路损伤自动检测提出解决方案。市政当局和道路机构可以利用RDD2022数据集，使用RDD2022训练的模型进行低成本的道路状况自动监测。此外，计算机视觉和机器学习研究人员可以使用该数据集对其他基于同一类型图像的应用程序（分类，对象检测等）的不同算法的性能进行基准测试。
The data article describes the Road Damage Dataset, RDD2022, which 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 dataset has been released as a part of the Crowd sensing-based Road Damage Detection Challenge (CRDDC2022). The challenge CRDDC2022 invites researchers from across the globe to propose solutions for automatic road damage detection in multiple countries. 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.).