Simple lane marking detection pipeline (Udacity nanodegree exercise - 2020)
Python, jupyter notebooks opencv, edge detection
- Loading the image
- Conversion to gray scale (for intensity filtering)
- Filtering pixels with high values (separating white lane markings from Background)
- Selecting a region of interest (cut out only area where our lane markings should be)
- Edge detection with canny edge algorithm
- Add Gaussian blur to smoothen and connect detected edges
- Using hough transform to generate lines out of our detected edges
- Extrapolate lines to solid lane boundarys that extend to the size of the ROI
- Annotating detected lines as lane markings on input image
Video: Simple lane detection pipeline
How did I implement it?
For the lane detection pipeline I used jupyter-notebooks to develop and visualize each step of the processing. It is programmed in python 3 and makes use of the image processing algorithms of the opencv library. If you are interested how each of the mentioned steps of this pipeline is implemented check out the project on my github. The project includes a writeup with more details about each processing step and the jupyter-notebook.