Detecting Lane Lines
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Lane Finding Project for Self-Driving Car Nano Degree Term 1. Road Lane Lines are detected by using various image processing techniques like Grayscale, Blurring, Canny Edge Detection, Hough Transform and Masking. ...learn more
Project status: Published/In Market
Groups
Student Developers for AI
Overview / Usage
Detecting Lane Lines on the road using opencv2.
Methodology / Approach
My Pipeline consisted of 6 steps:
Conversion of image to grayscale using the grayscale() function.
Applying Gaussian blur on the image using the gaussian_blur() function.
Converting image to an image with only edges using Canny edge detection. If high contrast in pixel value is found,it is considered as an edge and is detected using the canny() function.
To remove unwanted area, the image is masked to remove the unwanted portion using the region_of_interest() helper function.
Hough transform is used thereafter via Open CV method which helped connecting lines, we are interested in and in eliminating the rest. In this step we also used the draw_lines() method to add lines over the lanes in the image and extrapolate them.
Last step was to merge both the modified image and the original one to produce the result using the weighted_img() function.
Technologies Used
opencv2, image recognition