YOLO11-Rotonda

Video demonstration post

YOLO11-Rotonda is a Python-based project for real-time roundabout (rotonda) detection in traffic scenes using the YOLO (You Only Look Once) deep learning architecture. This repository provides a modular pipeline for training, evaluating, and deploying object detection models specifically tailored for roundabout identification and analysis, supporting intelligent transportation systems and autonomous driving applications.

Features

• State-of-the-Art Object Detection: Utilizes the YOLO architecture for high-speed, accurate detection of roundabouts in diverse traffic environments.
• End-to-End Pipeline: Includes data preprocessing, model training, inference, and evaluation scripts.
• Custom Dataset Support: Easily integrate your own labeled roundabout datasets.
• Real-Time Inference: Optimized for deployment in edge and real-time systems.
• Modular & Extensible: Clean codebase following Python best practices for easy adaptation and expansion.

Use Cases

• Autonomous Driving: Enhance vehicle situational awareness by reliably detecting roundabouts.
• Traffic Analysis: Support urban planning and infrastructure monitoring with automated detection.
• Research & Experimentation: Serve as a baseline for developing advanced object detection models in traffic domains.

Getting Started

Prerequisites

• Python 3.7+
• PyTorch (recommended)
• OpenCV
• NumPy
• Other dependencies as listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/ignacio-serrano-rodriguez/yolo11-rotonda.git
   cd yolo11-rotonda

2. Install dependencies:

   pip install -r requirements.txt

Usage

Training

Train your YOLO model on a roundabout dataset:

python train.py --data data/rotonda.yaml --cfg configs/yolovX.yaml --weights '' --batch-size 16

Inference

Run inference on images or video streams:

python detect.py --weights runs/train/exp/weights/best.pt --source path/to/images/

Evaluation

Evaluate model performance on a test set:

python test.py --data data/rotonda.yaml --weights runs/train/exp/weights/best.pt

Project Structure

.
├── data/           # Dataset configuration and samples
├── models/         # YOLO model definitions and configs
├── scripts/        # Utility and helper scripts
├── runs/           # Training outputs and logs
├── train.py        # Training entrypoint
├── detect.py       # Inference entrypoint
├── test.py         # Evaluation script
├── requirements.txt
└── README.md

Customization

• Dataset: Update data/rotonda.yaml to point to your custom dataset.
• Model Config: Modify or add configs in models/ as needed.
• Hyperparameters: Adjust training parameters in train.py or config files.

Contributing

Contributions are welcome! Please open issues or pull requests for bug fixes, improvements, or new features.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/YourFeature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature/YourFeature)
5. Open a pull request

Acknowledgements

• YOLO authors and the open-source community
• PyTorch, OpenCV, and related libraries

Contact

For questions, collaboration, or support, please contact Ignacio Serrano Rodriguez.