Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)

• URL: http://arxiv.org/abs/2211.11362v1
• Date: Mon, 21 Nov 2022 11:29:21 GMT
• Title: Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)
• Authors: Deeksha Arya (1), Hiroya Maeda (2), Sanjay Kumar Ghosh (3), Durga Toshniwal (3), Hiroshi Omata (1), Takehiro Kashiyama (4), Yoshihide Sekimoto (1) ((1) The University of Tokyo, Japan, (2) UrbanX Technologies, Inc., Tokyo, Japan (3) Indian Institute of Technology Roorkee, India, (4) Osaka University of Economics, Japan)
• Abstract summary: 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 data constitute 47,420 road images collected from India, Japan, the Czech Republic, Norway, the United States, and China. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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