Overview

Latest updates:

• December 5, 2022: The article summarizing the challenge CRDDC'2022 can be accessed here!
• December 5, 2022: The IEEE BigData workshop for CRDDC'2022 is scheduled for December 18, 2022. Checkout the schedule here.
• November 2, 2022: New leaderboards have been created to enable the users perform more experiments. Users need to login to access the submission links.
• October 31, 2022: The paper submissions have been closed!
• October 29, 2022: The RDD2022 data is now available as a citable resource at a publicly accessible data repository. Congratulations to all the contributors!
• October 16, 2022: The competition results have been announced. Congratulations to the winners!
• October 15, 2022: The shortlisted teams based on report and source code verification have been invited for the paper submission. Please check your email! The general guidelines for technical content in the paper are provided here.
• October 6, 2022: The submissions have been closed. Thank you for your participation!
• September 22, 2022: Deadline for Phase 3 and 4 has been extended! Submissions will be accepted till Oct 5, 2022.
• September 22, 2022: Submission links for phase 4 (Report and Source Code) have been enabled!
• September 20, 2022: The article for data released through CRDDC'2022 can be accessed here!
• August 30, 2022: Submission link for phase 3 has been enabled! Users need to LogIn to access!
• August 30, 2022: For users with queries related to Road Damage Categories: CRDDC involves following four damage categories: D00, D10, D20, and D40. The annotations for other categories may be ignored! Please refer to the article for more details!
• August 26, 2022: Submissions for phase 3 opens on August 30th, 2022!
• August 4, 2022: Main Task (for phase 3) has been announced. The data will be released on August 12, 2022.
• August 4, 2022: Result for Data Contribution phase has been declared. Congratulations to the winners!
• July 1, 2022: The deadline for phase 1 submissions is extended to July 20, 2022.
• May 30, 2022: Web site of the challenge opens, the task is revealed.

Description

Considering that roads have a direct and significant impact on people's lives, maintenance and management of roads need to be done exhaustively from time to time. However, a lack of financial resources makes many local governments unable to conduct sufficient inspections on time. Some municipalities automate road damage detection by using high-performance sensors. Nevertheless, the high cost of these sensors makes it infeasible to use them at the country level owing to the vast area of roads to be inspected

Therefore, there arises a need for a system that makes it easy to assess the road conditions and identify the damage to the road surface at a low cost. Recognizing this need, the first Road Damage Detection Challenge was organized as an IEEE Big Data Cup in 2018 to evaluate the contemporary methods working towards the same goal.

Another challenge (Global Road Damage Detection Challenge, GRDDC’2020) was organized in 2020 to address the requirements of multiple countries. GRDDC utilized data from India, Japan, and Czech Republic. More details may be accessed at (https://ieeexplore.ieee.org/abstract/document/9377790)

This year, another challenge is here with new opportunities. In previous challenges, the participants were restricted to using only the challenge dataset to train the models. This year, this constraint has been eliminated by allowing participants to develop/propose their datasets. The central theme of the challenge this year is "Crowdsensing," – which refers to the crowdsourcing of sensor data collected as per the choice of contributors.

Anyone having the dataset may register as a data contributor, data recommender, or information provider by participating in the first two phases of the challenge. After a suitability analysis, the selected datasets will be added officially to the CRDDC'2022 dataset and made available to the participants for phase 3.

Successful implementation of this challenge would open new possibilities where just the smartphones and drive cameras would be enough for road inspections, not for a single country but all countries worldwide.

The Task

The challenge will be organized in four phases with different tasks for the participants. The details are as follows.

Phase 1: Data Solicitation: The participants need to submit a report of their proposed road damage dataset (cracks, potholes, or any other damage categories) by providing the details through the corresponding forms. Following categories of participants are considered:

1. Data Contributors (Data owners who wish to contribute their data to CRDDC)
2. Information Providers (Data owners who wish to participate by providing only the information related to their dataset, keeping the data private)
3. Data Recommenders (Researchers/Academicians/Freelancers.... who wants to recommend the inclusion of others' datasets to CRDDC)

Phase 2: Data Shortlisting: Based on the reports submitted in phase 1, the shortlisted teams need to submit a link to their proposed dataset. After verification, the winners (data contributors) of phase 1 will be announced.

The solicited datasets will be added to the Challenge datasets for the next phase of the challenge.

Phase 3: Main Task: Use any combination of the CRDDC data and train road damage detection and classification models considering Objective 1, Objective 2 or both:

• Objective 1: Maximize accuracy (F1-Score) for individual countries (India, Japan, US, Norway)
• Objective 2: Maximize accuracy (F1-Score) for all countries (India, Japan, Czech, US, Norway, China).

The participants may propose models targetting one or multiple countries, concerning Objective 1 and 2. For instance - Model A for Country 1, Model B for Country 2, Model C for Countries 3 & 4, or a single model for all countries may be proposed. Average of score for all the leaderboards will be used to determine the winners.
Further, the challenge imposes no restrictions on the type of submission and welcome all the novel algorithms, techniques that are currently under review, and methods that have already been published. A summary of the models proposed by the participants of previous Big Data Cup (GRDDC'2020) may be accessed at (https://ieeexplore.ieee.org/abstract/document/9377790).

Phase 4: Report and Source Code: In phase 4, the participants need to submit a report of their proposed solution along with the source code. The submission will be used to finalize the winners

Note: After the competition phase is completed, a link for submitting the accompanying academic paper will be provided to the top 10 participants (may be increased based on the quality of submissions) as ranked by the public/private leaderboard weighting. For more details, please refer to the Submissions section.

Useful Links related to previous Road Damage Detection Challenge (GRDDC’2020):

1. GitHub Website for latest news and updates
2. ResearchGate Project for the latest publication updates
3. GRDDC Summary Paper – IEEE Conference Proceedings
4. GRDDC Summary Paper – Preprint – Open Access
5. GRDDC Workshop – Challenge Overview Video
6. GRDDC Workshop – Image Gallery()
7. Data Article for RDD2020 (Open Access)
8. RDD2020 Dataset on Mendeley
9. Other Related Articles( Article 1: RDD2020,   Article 2: RDD2019,   Article 3: RDD2018)

Citations

If you use the resources (data, content,…), please cite the following:

1. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., & Sekimoto, Y. (2022). Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022). arXiv preprint arXiv:2211.11362.
2. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., & Sekimoto, Y. (2022). RDD2022: A multi-national image dataset for automatic Road Damage Detection. arXiv preprint arXiv:2209.08538
3. Arya, D., Maeda, H., Sekimoto, Y., Omata, H., Ghosh, S. K., Toshniwal, D., Sharma, M., Pham, V. V., Zhong, J., et al. (2022). RDD2022 - The multi-national Road Damage Dataset released through CRDDC'2022 (Version 1). figshare. https://doi.org/10.6084/m9.figshare.21431547.v1
4. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Mraz, A., Kashiyama, T., & Sekimoto, Y. (2021). Deep learning-based road damage detection and classification for multiple countries. Automation in Construction, 132, 103935. 10.1016/j.autcon.2021.103935
5. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., & Sekimoto, Y. (2021). RDD2020: An annotated image dataset for Automatic Road Damage Detection using Deep Learning. Data in Brief, 107133. <https://doi.org/10.1016/j.dib.2021.107133>.
6. Arya, D., Maeda, H., Ghosh, S. K., Toshniwal, D., Omata, H., Kashiyama, T., & Sekimoto,Y. (2020). Global Road Damage Detection: State-of-the-art Solutions. IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 5533-5539, doi: 10.1109/BigData50022.2020.9377790.
7. Maeda, H., Kashiyama, T., Sekimoto, Y., Seto, T., & Omata, H. (2021). Generative adversarial network for road damage detection. Computer‐Aided Civil and Infrastructure Engineering, 36(1), 47-60.

Important Dates

Tentative Timeline:

**** It may be noted that the participants who do not have any data to contribute may choose to participate directly in Phase 3 or may suggest the inclusion of data available from others to participate in Phase I (as Data Recommenders). For those already having their data (private or public), it would be an added benefit to release the data through the CRDDC since the data would reach a global audience through a well-established platform. ****

Important dates: (Updated - 05/12/2022)