论文标题
基于人群的道路伤害检测挑战（CRDDC-2022）
Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)
论文作者
论文摘要
本文总结了基于人群的道路伤害检测挑战（CRDDC），这是一个大数据杯，是IEEE国际大数据2022年国际会议的一部分。大数据杯挑战涉及发布的数据集和明确的评估指标定义明确的问题。这些挑战在数据竞争平台上进行，该平台维护参与者的实时在线评估系统。在介绍的情况下，数据构成了从印度，日本，捷克共和国，挪威，美国和中国收集的47,420条道路图像，提出了自动检测这些国家 /地区道路损失的方法。来自19个国家 /地区的60多个团队参加了这场比赛。根据五个排行榜，根据上述六个国家的看不见的测试图像进行了五个排行榜，对提交的解决方案进行了评估。本文封装了这些团队提出的前11个解决方案。表现最佳的模型利用基于Yolo和更快的RCNN系列模型的合奏学习，从所有6个国家 /地区的测试数据中产生76％的F1分数。本文结束了当前和过去的挑战的比较，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 60 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.
