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Finding Lane Lines on the Road

Finding Lane Lines on the Road

The goals / steps of this project are the following:

  • Make a pipeline that finds lane lines on the road

Reflection

1. Describe your pipeline. As part of the description, explain how you modified the draw_lines() function.

My pipeline is composed of several algorithems and functions, apply them to an image, and produce an annotated image that shows where a lane on a road would be.

My pipeline implementation is summarized in the following steps:

  • Applying a color mask by converting RGB to HSV
  • filter Yellow and white lines from HSV plane
  • Apply Gaussian blur on the resulted yellow and white mask
  • Perform edge detection
  • Define region of interest to search for lane lines
  • Using the Hough transform to find line segments
  • Consolidate and Extrapolate the lane from the line segments and apply to original image

Color Masking

HSV Color Space

Using cv2.cvtColor, we can convert RGB image into HSV color space.

alt text

Build the filter to apply on image to get white and yellow lines.

Using cv2.inRange build the filter to seperate yellow and white lines.

Using Canny Edge Detection and Gaussian blur

I used gaussian blur to smooth out the edges by reducing the noise.The important parameter is kernel_size, bigger kernel_size means more blurred image. I used Canny edge detection to identify straight lines by detecting the edges from the given image.The Canny edge function takes a high and low threshold as a parameters — (canny_high_threshold - minimum difference in intensity to establish an edge and canny_low_threshold - to form a contiguous extension of an established edge)

  • use opencv function cv2.GaussianBlur to remove noise from the edges.
  • use opencv function cv2.Canny to find edges

To identifing the right values for the parameters to produce desired output,I first set the canny_low_threshold to zero and then adjust the canny_high_threshold. If canny_high_threshold is too high, you find no edges and if it is too low then you will see too many edges.later adjust the canny_low_threshold to discard the noise connected to the strong edges.

Define Region of Interest (ROI) Selection

Only keeps the region of the image defined by the polygon formed from vertices and exclude outside region of interest by setting it to black.

Using the Hough transform to find line segments

The hough transform that actually finds line segments in the image and provides the most information of where the lanes lines could be. It takes a resolution for line position and orientation, a minimum number of points to establish a line, the minimum length of a line, and the maximum gap between points allowed for a line as parameters.Tuning the parameters was crucial part of the pipeline.

Consolidate and Extrapolate the lane

Once we have the line segments produced by the hough transform, we can find a line that would be suitable for annotating and extrapolating the image.

2. Identify potential shortcomings with your current pipeline

The challenge video exposed a couple flaws with my pipeline:

Highly sensitive to image contrast
pipeline stumbles during curve path

3. Suggest possible improvements to your pipeline

Identify the right parameters for hough transform and edge detection.Way to identify at run-time.