ProjectRoadDamageDetection/Automated-Road-Damage-Detection

🛣️ Automated Road Damage Detection

YOLOv8s fine-tuned on the RDD2022 dataset — detecting 4 road damage classes across 6 countries in real time.

   

📌 Overview

This project provides an automated road damage detection system powered by YOLOv8s, fine-tuned on the RDD2022 (Road Damage Dataset 2022). It detects four types of surface damage from road images and provides per-class counts, pothole severity assessments, and country-specific pothole density context.

Upload any road image and get instant visual annotations along with a structured damage report.

🎯 Damage Classes

🌍 Supported Countries (Pothole Context)

🚀 How to Use

1. 1.Upload a road image using the image input panel (drag & drop, camera capture, or paste from clipboard).
2. 2.Adjust thresholds (optional):
3. 3.Confidence Threshold (default: 0.25) — minimum detection confidence score.
4. 4.IoU Threshold (default: 0.45) — suppresses overlapping boxes.
5. 5.Select the country where the photo was taken for pothole density context.
6. 6.Click 🔍 Detect Damage (or the image auto-triggers detection on upload).
7. 7.View the annotated output image + Detection Summary, Pothole Report, and Country Context tables.

📊 Output Explained

Detection Summary

A per-class breakdown table showing:

• —Count of each damage type detected
• —Percentage of total detections

Pothole (D40) Report

Severity thresholds:

• —🔴 CRITICAL — Potholes > 30% of all detections → Immediate repair needed
• —🟠 MODERATE — Potholes > 10% → Schedule maintenance
• —🟡 LOW — Any potholes detected → Monitor road surface
• —🟢 NONE DETECTED — Road surface OK

Country Context

Provides pothole density, dominant damage types, and data collection method for the selected country as documented in the RDD2022 dataset.

🧠 Model Details

📁 Dataset — RDD2022

• —Total images: 47,420
• —Countries: India, Japan, United States, Czech Republic, China, Norway
• —Annotation format: Bounding boxes (YOLO format)
• —Reference: Arya et al., 2022 — arxiv:2209.08538

🗂️ Repository Structure

Automated-Road-Damage-Detection/
├── app.py                  # Gradio UI + inference pipeline
├── predict.py              # Standalone prediction script
├── yolov8s_best.pt         # Fine-tuned YOLOv8s weights (22.5 MB)
├── rdd2022.yaml            # Dataset config (class names, paths)
├── requirements.txt        # Python dependencies
├── examples/               # Sample road images (6 countries)
│   ├── india_test_image.jpg
│   ├── China_Drone_000253.jpg
│   ├── United_States_004798.jpg
│   ├── Czech_test_image.jpg
│   ├── China_Drone_000295.jpg
│   └── norway_road_test.jpg
└── README.md

🔧 Local Setup

# Clone the Space
git clone https://huggingface.co/spaces/ProjectRoadDamageDetection/Automated-Road-Damage-Detection
cd Automated-Road-Damage-Detection

# Install dependencies
pip install -r requirements.txt

# Run the app
python app.py

The app will be available at http://localhost:7860.

📦 Requirements

gradio>=4.0
ultralytics
torch
torchvision
numpy
Pillow
opencv-python

📄 License

This project is licensed under the MIT License — see the LICENSE for details.

📚 Citation

If you use this work or the RDD2022 dataset, please cite:

@article{arya2022rdd2022,
  title   = {RDD2022: A multi-national image dataset for automatic Road Damage Detection},
  author  = {Arya, Deeksha and Maeda, Hiroya and Ghosh, Sanjay Kumar and Toshniwal, Durga and Mraz, Alexander and Kashiyama, Takehiro and Sekimoto, Yoshihide},
  journal = {arXiv preprint arXiv:2209.08538},
  year    = {2022}
}

🙌 Acknowledgements

• —Ultralytics YOLOv8 for the object detection framework
• —RDD2022 Dataset for the training data
• —Gradio for the web interface