/road-lane-detection

Road Lane Detection using Hough Transform

Primary LanguageJupyter NotebookMIT LicenseMIT

Road Lane Detection

A manual implementation of the Hough transform to aid in the task of road lane detection.

Original Image

This project was developed as part of the course Computer Vision in the Fall 2022 semester at the Faculty of Engineering, Alexandria University, under the Computer and Communications Engineering department, supervised by Dr. Marwan Torki.

Steps

1- Smoothing the image using a 2-dimensional median smoothing filter.

Smoothed Image

2- Edge Detection using Canny’s algorithm.

Edge Detected Image

3- Region Of Interest selection.

Region of Interest Image

4- Accumulation into (ρ, θ)-space using Hough transform.

Detected Lane Image

Prerequisites

This project was developed in the following environment:

  • Jupyter Notebook
  • Miniconda
  • Python 3.11.5

Installing

1- Clone the repository to your local machine:

git clone https://github.com/MohEsmail143/road-lane-detection.git

2- Open Jupyter notebook.

3- Check out the the Jupyter notebook road_lane_detection.ipynb.

License

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