This paper summarizes the Global Road Damage Detection Challenge (GRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2020. 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 leaderboard for the participants. In the presented case, the data constitute 26336 road images collected from India, Japan, and the Czech Republic to propose methods for automatically detecting road damages in these countries. In total, 121 teams from several countries registered for this competition. The submitted solutions were evaluated using two datasets test1 and test2, comprising 2,631 and 2,664 images. This paper encapsulates the top 12 solutions proposed by these teams. The best performing model utilizes YOLO-based ensemble learning to yield an F1 score of 0.67 on test1 and 0.66 on test2. The paper concludes with a review of the facets that worked well for the presented challenge and those that could be improved in future challenges.

中文翻译：

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全球道路损坏检测：最先进的解决方案

本文总结了全球道路损坏检测挑战赛 (GRDDC)，这是作为 IEEE 大数据国际会议 2020 的一部分组织的大数据杯。大数据杯挑战涉及已发布的数据集和具有明确评估指标的明确问题。挑战在一个数据竞赛平台上运行，该平台为参与者维护一个排行榜。在本案例中，数据构成了从印度、日本和捷克共和国收集的 26336 张道路图像，以提出在这些国家自动检测道路损坏的方法。共有来自多个国家的121支队伍报名参加了本次比赛。提交的解决方案使用两个数据集 test1 和 test2 进行评估，包括 2,631 和 2,664 张图像。本文概括了这些团队提出的前 12 个解决方案。性能最好的模型利用基于 YOLO 的集成学习在 test1 上产生 0.67 的 F1 分数，在 test2 上产生 0.66。本文最后回顾了对当前挑战有效的方面以及在未来挑战中可以改进的方面。