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Lane Line Detection Using Image Processing 🛣️

Introduction

This project implements lane line detection using image processing techniques. The workflow involves several key steps, including color selection, gray scaling, Gaussian smoothing, edge detection, and the Hough transform to detect lane lines on the road. The process can handle both images and video feeds, making it adaptable to real-time applications such as autonomous driving systems.

Workflow Diagram ☕

edge detection workflow

Workflow Overview 📝

1. Color Selection:

Choose the appropriate color space (RGB, HSV, or HSL) for lane detection.

2. Gray Scaling:

Convert the image to grayscale to reduce complexity.

3. Gaussian Smoothing:

Apply Gaussian filtering to reduce noise in the image.

4. Edge Detection:

Use the Canny edge detection method to identify the edges of lane lines.

5. Region of Interest:

Define the region of interest to focus on the lane area.

6. Hough Transform:

Detect lines in the edge-detected image using the Hough transform.

7. Averaging and Extrapolating:

Refine the detected lane lines for clear output.

Requirements 🧑‍💻

  • Python 3.x
  • Jupyter Notebook
  • OpenCV (cv2)
  • NumPy
  • Matplotlib
  • Moviepy

Installation 📩

  1. Clone the repository to your local machine: 
    git clone https://github.com/manojsiddoji/Edge_detection-using-Image_Processing.git
  2. Navigate to the project directory: 
    cd Edge_detection-using-Image_Processing
  3. Install the required Python packages: 
    pip install [all the requirements]
  4. Launch Jupyter Notebook: 
    jupyter notebook
  5. Open the Final.ipynb file from the Jupyter interface to run the lane line detection workflow step-by-step.

License

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

License: MIT