Arya Deeksha, Maeda Hiroya, Ghosh Sanjay Kumar, Toshniwal Durga, Sekimoto Yoshihide
Center for Transportation Systems, Indian Institute of Technology Roorkee, 247667, India.
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Tokyo, Japan.
Data Brief. 2021 May 12;36:107133. doi: 10.1016/j.dib.2021.107133. eCollection 2021 Jun.
This data article provides details for the RDD2020 dataset comprising 26,336 road images from India, Japan, and the Czech Republic with more than 31,000 instances of road damage. The dataset captures four types of road damage: longitudinal cracks, transverse cracks, alligator cracks, and potholes; and is intended for developing deep learning-based methods to detect and classify road damage automatically. The images in RDD2020 were captured using vehicle-mounted smartphones, making it useful for municipalities and road agencies to develop methods for low-cost monitoring of road pavement surface conditions. Further, the machine learning researchers can use the datasets for benchmarking the performance of different algorithms for solving other problems of the same type (image classification, object detection, etc.). RDD2020 is freely available at [1]. The latest updates and the corresponding articles related to the dataset can be accessed at [2].
本数据文章提供了RDD2020数据集的详细信息，该数据集包含来自印度、日本和捷克共和国的26336张道路图像，有超过31000个道路损坏实例。该数据集捕捉了四种类型的道路损坏：纵向裂缝、横向裂缝、龟裂和坑洼；旨在开发基于深度学习的方法来自动检测和分类道路损坏。RDD2020中的图像是使用车载智能手机拍摄的，这对于市政当局和道路机构开发低成本监测路面状况的方法很有用。此外，机器学习研究人员可以使用这些数据集来基准测试不同算法解决其他同类问题（图像分类、目标检测等）的性能。RDD2020可在[1]免费获取。与该数据集相关的最新更新和相应文章可在[2]访问。
