# Global Road Damage Detection: State-of-the-art Solutions

arXiv: https://arxiv.org/abs/2011.08740 · HTML: https://mlsanity.com/paper/2011.08740/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 16 of 16)

- [RDD2022: A multi-national image dataset for automatic Road Damage Detection](https://mlsanity.com/paper/2209.08538/) (2022)
- [Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)](https://mlsanity.com/paper/2211.11362/) (2022)
- [The 1st Data Science for Pavements Challenge](https://mlsanity.com/paper/2206.04874/) (2022)
- [Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey](https://mlsanity.com/paper/2402.04297/) (2024)
- [Deep Learning Approaches in Pavement Distress Identification: A Review](https://mlsanity.com/paper/2308.00828/) (2023)
- [AI Driven Road Maintenance Inspection](https://mlsanity.com/paper/2106.02567/) (2021)
- [Multiple data sources and domain generalization learning method for road surface defect classification](https://mlsanity.com/paper/2407.10197/) (2024)
- [CNN Model & Tuning for Global Road Damage Detection](https://mlsanity.com/paper/2103.09512/) (2021)
- [AI-Driven Road Maintenance Inspection v2: Reducing Data Dependency & Quantifying Road Damage](https://mlsanity.com/paper/2210.03570/) (2022)
- [Road Damages Detection and Classification with YOLOv7](https://mlsanity.com/paper/2211.00091/) (2022)
- [Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing](https://mlsanity.com/paper/2402.04064/) (2024)
- [Optimizing YOLO Architectures for Optimal Road Damage Detection and Classification: A Comparative Study from YOLOv7 to YOLOv10](https://mlsanity.com/paper/2410.08409/) (2024)
- [M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data](https://mlsanity.com/paper/2504.10123/) (2025)
- [Road Rutting Detection using Deep Learning on Images](https://mlsanity.com/paper/2209.14225/) (2022)
- [A Computer Vision Enabled damage detection model with improved YOLOv5 based on Transformer Prediction Head](https://mlsanity.com/paper/2303.04275/) (2023)
- [Road Disease Detection based on Latent Domain Background Feature Separation and Suppression](https://mlsanity.com/paper/2309.07616/) (2023)

## Abstract (arXiv)

This paper summarizes the Global Road Damage Detection Challenge (GRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2020. 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 leaderboard for the participants. In the presented case, the data constitute 26336 road images collected from India, Japan, and the Czech Republic to propose methods for automatically detecting road damages in these countries. In total, 121 teams from several countries registered for this competition. The submitted solutions were evaluated using two datasets test1 and test2, comprising 2,631 and 2,664 images. This paper encapsulates the top 12 solutions proposed by these teams. The best performing model utilizes YOLO-based ensemble learning to yield an F1 score of 0.67 on test1 and 0.66 on test2. The paper concludes with a review of the facets that worked well for the presented challenge and those that could be improved in future challenges.