RDD2022:用于道路损坏自动检测的多国图像数据集

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh, Durga Toshniwal, Yoshihide Sekimoto
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引用次数: 0

摘要

这篇文章介绍了道路损坏数据集 RDD2022,其中包括来自日本、印度、捷克共和国、挪威、美国和中国六大国家的 47,420 幅道路图像。该数据集包含超过 55000 个道路损坏实例,特别是纵向裂缝、横向裂缝、鳄鱼裂缝和坑洞。RDD2022 旨在促进道路损伤自动检测和分类的深度学习方法的发展,作为基于人群感知的道路损伤检测挑战赛(CRDDC'2022)的一部分,RDD2022 的发布得到了挑战赛优胜者的大力支持。该挑战赛吸引了全球的参与,敦促多个国家的研究人员提出道路损坏自动检测的解决方案。CRDDC'2022 的一个值得注意的成果是,在使用 RDD2022 检测所有六个国家的道路损坏时,出现了一个性能最佳的模型,其 F1 分数高达 76.9%。这一成功强调了该数据集对市政和道路机构的实际适用性,实现了对道路状况的低成本自动监测。除了其直接实用性,RDD2022 还是计算机视觉、地球科学和机器学习研究人员的宝贵基准,为分类和物体检测等各种基于图像的应用中的算法评估提供了丰富的资源。最新的大数据杯赛 "优化道路损坏检测挑战赛(ORDDC'2024)"也是以 RDD2022 为基础的,凸显了其在当前研发工作中的持续相关性和关键作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

RDD2022: A multi-national image dataset for automatic road damage detection

RDD2022: A multi-national image dataset for automatic road damage detection

The data article describes the Road Damage Dataset, RDD2022, encompassing of 47,420 road images from majorly six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The dataset incorporates over 55,000 instances of road damage, specifically longitudinal cracks, transverse cracks, alligator cracks, and potholes. Designed to facilitate the development of deep learning methodologies for automated road damage detection and classification, RDD2022 was unveiled as part of the Crowd sensing-based Road Damage Detection Challenge (CRDDC'2022), with a major contribution from the challenge winners. This challenge garnered global participation, urging researchers to propose solutions for automatic road damage detection in multiple countries. A noteworthy outcome of CRDDC'2022 was the emergence of a top-performing model achieving a remarkable F1 Score of 76.9% for road damage detection in all six countries using RDD2022. This success underscores the dataset's practical applicability for municipalities and road agencies, enabling low-cost, automatic monitoring of road conditions. Beyond its immediate utility, RDD2022 stands as a valuable benchmark for researchers in computer vision, geoscience, and machine learning, offering a rich resource for algorithmic evaluation in diverse image-based applications, including classification and object detection. The latest big data cup, Optimized Road Damage Detection Challenge (ORDDC'2024), is also based on RDD2022, underscoring its continued relevance and pivotal role in current research and development endeavors.

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来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍: Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
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