📋 Overview

A robust lane detection system that identifies and tracks road lane markings in real-time using computer vision techniques. This project processes video streams from dashcam footage and overlays detected lane boundaries, providing a visual guide for autonomous vehicle systems or driver assistance applications.

✨ Features

• 🎥 Real-time Processing - Processes video streams with minimal latency
• 🎯 Accurate Detection - Uses advanced edge detection and Hough transforms
• 🖼️ Region of Interest (ROI) - Focuses on relevant road areas for improved performance
• 📊 Lane Overlay - Visual representation of detected lanes on original footage
• ⚡ Efficient Algorithm - Optimized for performance on standard hardware
• 🔧 Configurable Parameters - Easy tuning for different road conditions

🎬 Demo

The system processes dashcam footage and outputs video with detected lane markings highlighted in real-time.

Input: Road view from vehicle camera (road_car_view.mp4)
Output: Processed video with lane overlay (out.gif)

🚀 Installation

Prerequisites

• Python 3.7 or higher
• pip package manager

Setup

1. Clone the repository

   git clone https://github.com/Devatva24/Lane-Detection.git
   cd Lane-Detection

2. Install required dependencies

   pip install opencv-python numpy matplotlib

💻 Usage

Running the Lane Detection

python lane_detection_codespace.py

The script will:

1. Load the input video file (road_car_view.mp4)
2. Process each frame to detect lane markings
3. Save the output with detected lanes

Customization

You can modify parameters in the script to adjust:

• Canny edge detection thresholds
• Hough transform parameters
• Region of interest coordinates
• Line drawing style and color

🔬 How It Works

The lane detection pipeline consists of several key steps:

1. Preprocessing

• Convert frames to grayscale
• Apply Gaussian blur to reduce noise

2. Edge Detection

• Use Canny edge detection to identify lane boundaries
• Highlight areas with significant intensity changes

3. Region of Interest

• Define a polygonal mask to focus on the road area
• Filter out irrelevant portions of the image

4. Line Detection

• Apply Hough transform to detect straight lines
• Identify lane markings from detected edges

5. Lane Extrapolation

• Average and extrapolate detected line segments
• Create smooth, continuous lane boundaries

6. Overlay & Output

• Draw detected lanes on the original frame
• Combine with input video for final output

📁 Project Structure

Lane-Detection/
├── lane_detection_codespace.py    # Main detection script
├── road_car_view.mp4               # Sample input video
├── out.gif                         # Output demonstration
├── README.md                       # Project documentation
├── .gitignore                      # Git ignore rules
└── .gitattributes                  # Git attributes

🛠️ Technologies Used

• Python - Core programming language
• OpenCV - Computer vision library for image processing
• NumPy - Numerical computing for array operations
• Matplotlib - Visualization and debugging (optional)

📈 Future Enhancements

• Curved lane detection using polynomial fitting
• Multi-lane detection support
• Night-time and adverse weather conditions
• Real-time webcam input support
• Integration with deep learning models (YOLO, U-Net)
• Lane departure warning system
• Performance optimization using GPU acceleration

🤝 Contributing

Contributions are welcome! Here's how you can help:

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

📝 License

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

👤 Author

Devatva24

• GitHub: @Devatva24

🙏 Acknowledgments

• Inspired by autonomous vehicle research and ADAS systems
• Built using open-source computer vision libraries
• Special thanks to the OpenCV community