🚗 Real-Time Vehicle Speed Detection using YOLOv8 & OpenCV

A real-time vehicle speed detection system built using Python, OpenCV, and Ultralytics YOLOv8. The application detects and tracks vehicles in a video stream, measures their speed between predefined reference lines, and displays the calculated speed in real time.

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✨ Features

• 🚘 Real-time vehicle detection using YOLOv8
• 🎯 Multi-object tracking with unique IDs
• ⚡ Speed estimation in km/h
• 📹 Supports video files and RTSP/IP camera streams
• 📊 Bounding boxes with vehicle ID and speed
• 🛣️ Configurable detection lines/regions
• 🐍 Simple Python implementation

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🛠️ Technologies Used

• Python 3.12
• OpenCV
• Ultralytics YOLOv8
• Nextjs

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📂 Project Structure

Speed-Detection/
│
├── backend/
│   ├── main.py
│   ├── speed_detection.py
│   ├── orientation.py
│   └── ...
│
├── frontend (Nextjs)
├── README.md
└── .gitignore

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📸 Sample Results

Screenshots and demo videos are available inside the samples/ folder.

samples/

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⚙️ Installation

1. Install Python

Download and install Python 3.12 from:

https://www.python.org/downloads/

During installation, make sure to enable:

✔ Add Python to PATH

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2. Clone the Repository

git clone https://github.com/Mavis47/Speed-Detection.git

cd Speed-Detection

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3. Create a Virtual Environment

Windows

python -m venv venv

venv\Scripts\activate

Linux/macOS

python3 -m venv venv

source venv/bin/activate

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4. Install Dependencies

pip install opencv-python

pip install ultralytics

or install everything together

pip install -r requirements.txt

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▶️ Run the Project

python backend/main.py

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📦 Dependencies

• Python 3.12
• OpenCV
• Ultralytics

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📹 Input

The system supports

• MP4 Videos
• AVI Videos
• RTSP Streams
• IP Cameras

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🚘 Output

The application displays

• Vehicle Detection
• Vehicle Tracking ID
• Estimated Vehicle Speed (km/h)
• Bounding Boxes
• Detection Lines

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📁 Sample Media

Example screenshots and videos can be found in

samples

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🤝 Contributing

Contributions are welcome.

1. Fork the repository
2. Create a new branch
3. Commit your changes
4. Open a Pull Request

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⭐ Support

If you found this project useful, consider giving it a ⭐ on GitHub.