  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.
