# RDD2022: A multi-national image dataset for automatic Road Damage Detection

arXiv: https://arxiv.org/abs/2209.08538 · HTML: https://mlsanity.com/paper/2209.08538/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 18 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone](https://mlsanity.com/paper/1801.09454/) (2018)
- [Global Road Damage Detection: State-of-the-art Solutions](https://mlsanity.com/paper/2011.08740/) (2020)
- [Transfer Learning-based Road Damage Detection for Multiple Countries](https://mlsanity.com/paper/2008.13101/) (2020)

## Built on by (most cited first, 30 of 32)

- [Does Image Anonymization Impact Computer Vision Training?](https://mlsanity.com/paper/2306.05135/) (2023)
- [Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)](https://mlsanity.com/paper/2211.11362/) (2022)
- [Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey](https://mlsanity.com/paper/2402.04297/) (2024)
- [YOLO9tr: A Lightweight Model for Pavement Damage Detection Utilizing a Generalized Efficient Layer Aggregation Network and Attention Mechanism](https://mlsanity.com/paper/2406.11254/) (2024)
- [V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?](https://mlsanity.com/paper/2408.10872/) (2024)
- [Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection](https://mlsanity.com/paper/2511.12410/) (2025)
- [Multiple data sources and domain generalization learning method for road surface defect classification](https://mlsanity.com/paper/2407.10197/) (2024)
- [Automated Road Safety: Enhancing Sign and Surface Damage Detection with AI](https://mlsanity.com/paper/2407.15406/) (2024)
- [Integrating GAN and Texture Synthesis for Enhanced Road Damage Detection](https://mlsanity.com/paper/2309.06747/) (2023)
- [Leveraging AI for Natural Disaster Management : Takeaways From The Moroccan Earthquake](https://mlsanity.com/paper/2311.08999/) (2023)
- [Improving classification of road surface conditions via road area extraction and contrastive learning](https://mlsanity.com/paper/2407.14418/) (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)
- [Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms](https://mlsanity.com/paper/2503.18082/) (2025)
- [M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data](https://mlsanity.com/paper/2504.10123/) (2025)
- [What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies](https://mlsanity.com/paper/2507.06513/) (2025)
- [PaveSync: A Unified and Comprehensive Dataset for Pavement Distress Analysis and Classification](https://mlsanity.com/paper/2512.20011/) (2025)
- [StructDamage:A Large Scale Unified Crack and Surface Defect Dataset for Robust Structural Damage Detection](https://mlsanity.com/paper/2603.10484/) (2026)
- [CARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography](https://mlsanity.com/paper/2605.05014/) (2026)
- [Road Rutting Detection using Deep Learning on Images](https://mlsanity.com/paper/2209.14225/) (2022)
- [Fusing Pseudo Labels with Weak Supervision for Dynamic Traffic Scenarios](https://mlsanity.com/paper/2308.15960/) (2023)
- [Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly Systems](https://mlsanity.com/paper/2504.13648/) (2025)
- [Intelligent Road Anomaly Detection with Real-time Notification System for Enhanced Road Safety](https://mlsanity.com/paper/2505.08882/) (2025)
- [AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directions](https://mlsanity.com/paper/2507.01547/) (2025)
- [YOLO-ROC: A High-Precision and Ultra-Lightweight Model for Real-Time Road Damage Detection](https://mlsanity.com/paper/2507.23225/) (2025)
- [Joint Training of Image Generator and Detector for Road Defect Detection](https://mlsanity.com/paper/2509.03465/) (2025)
- [A Semantic Observer Layer for Autonomous Vehicles: Pre-Deployment Feasibility Study of VLMs for Low-Latency Anomaly Detection](https://mlsanity.com/paper/2603.28888/) (2026)
- [PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysis](https://mlsanity.com/paper/2604.02804/) (2026)
- [Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment](https://mlsanity.com/paper/2604.08212/) (2026)
- [WildRoadBench: A Wild Aerial Road-Damage Grounding Benchmark for Vision-Language Models and Autonomous Agents](https://mlsanity.com/paper/2605.20306/) (2026)
- [A Digital Twin Framework for Traffic-Aware UAV Pavement Monitoring in Open-Traffic Conditions](https://mlsanity.com/paper/2606.20742/) (2026)

## Abstract (arXiv)

The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The images have been annotated with more than 55,000 instances of road damage. Four types of road damage, namely longitudinal cracks, transverse cracks, alligator cracks, and potholes, are captured in the dataset. The annotated dataset is envisioned for developing deep learning-based methods to detect and classify road damage automatically. The dataset has been released as a part of the Crowd sensing-based Road Damage Detection Challenge (CRDDC2022). The challenge CRDDC2022 invites researchers from across the globe to propose solutions for automatic road damage detection in multiple countries. The municipalities and road agencies may utilize the RDD2022 dataset, and the models trained using RDD2022 for low-cost automatic monitoring of road conditions. Further, computer vision and machine learning researchers may use the dataset to benchmark the performance of different algorithms for other image-based applications of the same type (classification, object detection, etc.).