mlsanityData to 14 Oct 2026

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

arXiv 2211.11362 ↗First posted 21 Nov 2022cs.CV cs.AIcited by 9

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 24 of its 61 references that are in the map. First posted on arXiv 21 Nov 2022.

Builds on

24 of 61 references.

References · 24

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12 more
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  4. 2018Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphonecited by 45
  5. 2022RDD2022: A multi-national image dataset for automatic Road Damage Detectioncited by 32
  6. 2020Global Road Damage Detection: State-of-the-art Solutionscited by 16
  7. 2020An Efficient and Scalable Deep Learning Approach for Road Damage Detectioncited by 7
  8. 2020Transfer Learning-based Road Damage Detection for Multiple Countriescited by 6
  9. 2020Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysiscited by 6
  10. 2020Road Damage Detection using Deep Ensemble Learningcited by 5
  11. 2022The 1st Data Science for Pavements Challengecited by 4
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 1.

9 papers.

  1. 2024Multiple data sources and domain generalization learning method for road surface defect classificationcited by 2
  2. 2024Optimizing YOLO Architectures for Optimal Road Damage Detection and Classification: A Comparative Study from YOLOv7 to YOLOv10cited by 1
  3. 2025RDD4D: 4D Attention-Guided Road Damage Detection And Classificationcited by 1
  4. 2025What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studiescited by 1
  5. 2024Cut-and-Paste with Precision: a Content and Perspective-aware Data Augmentation for Road Damage Detectioncited by 0
  6. 2025Intelligent Road Anomaly Detection with Real-time Notification System for Enhanced Road Safetycited by 0
  7. 2025AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directionscited by 0
  8. 2025Joint Training of Image Generator and Detector for Road Defect Detectioncited by 0
  9. 2026AgrI Challenge: A Data-Centric AI Competition for Cross-Team Validation in Agricultural Visioncited by 0