Crowdsensing-based Road Damage Detection Challenge (CRDDC’2022)
Crowdsensing-based Road Damage Detection Challenge (CRDDC’2022)
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DOI：
10.1109/bigdata55660.2022.10021040
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发表时间：
2022-11
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通讯作者：
Deeksha M. Arya;Hiroya Maeda;S. Ghosh;Durga Toshniwal;Hiroshi Omata;Takehiro Kashiyama;Yoshihide Sekimoto The University of Tokyo;Japan.;UrbanX Technologies;Inc.;Tokyo;Japan Indian Institute of Technology Roorkee;India;Osaka University of Economics
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文献类型：
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作者：
Deeksha M. Arya;Hiroya Maeda;S. Ghosh;Durga Toshniwal;Hiroshi Omata;Takehiro Kashiyama;Yoshihide Sekimoto The University of Tokyo;Japan.;UrbanX Technologies;Inc.;Tokyo;Japan Indian Institute of Technology Roorkee;India;Osaka University of Economics
本文总结了基于人群传感的道路损坏检测挑战赛（CRDDC），这是作为IEEE 2022年大数据国际会议的一部分组织的大数据杯。大数据杯的挑战涉及一个已发布的数据集和一个定义明确的问题，以及明确的评估指标。挑战赛在一个数据竞赛平台上进行，该平台为参赛者提供实时在线评估系统。在本案例中，数据构成47，420道路图像从印度，日本，捷克共和国，挪威，美国和中国收集，提出自动检测这些国家的道路损坏的方法。来自19个国家的70多支队伍报名参加了本次比赛。提交的解决方案使用五个排行榜进行评估，这些排行榜基于来自上述六个国家的不可见测试图像的性能。本文概括了这些团队提出的11个最佳解决方案。性能最好的模型利用基于YOLO和Faster-RCNN系列模型的集成学习，对来自所有6个国家的测试数据组合产生76%的F1分数。本文最后比较了当前和过去的挑战，并为未来提供了方向。
This paper summarizes the Crowdsensing-based Road Damage Detection Challenge (CRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data’2022. The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics. The challenges run on a data competition platform that maintains a real-time online evaluation system for the participants. In the presented case, the data constitute 47,420 road images collected from India, Japan, the Czech Republic, Norway, the United States, and China to propose methods for automatically detecting road damages in these countries. More than 70 teams from 19 countries registered for this competition. The submitted solutions were evaluated using five leaderboards based on performance for unseen test images from the aforementioned six countries. This paper encapsulates the top 11 solutions proposed by these teams. The best-performing model utilizes ensemble learning based on YOLO and Faster-RCNN series models to yield an F1 score of 76% for test data combined from all 6 countries. The paper concludes with a comparison of current and past challenges and provides direction for the future.