Lane-Line Detection System in Python using OpenCV

Being able to detect lane lines could be a crucial task for any self-driving autonomous vehicle. In this project, to identify lane lines on the road OpenCV is used. OpenCV method uses the input images to find any lane lines command among and also for rendering out an illustration of the lane. The OpenCV tools like colour selection, the region of interest selection, grey scaling, Gaussian smoothing, Canny Edge Detection, and Hough Transform line detection are being employed. A colour detection algorithm identifies pixels in a picture that matches a given colour or colour range. Region of interest selection allows you to select a rectangle in an image, crop the rectangular region and finally display the cropped image. Grey scaling is the method of changing an image from different colour spaces e.g. RGB, CMYK, HSV, etc. to shades of grey. In gaussian Blur operation, the image is convolved with a mathematician filter rather than the box filter. The Gaussian filter could be a low-pass filter that removes the high-frequency elements. Canny Edge Detection is used to detect the edges in a picture. It accepts a grayscale image as input and it uses a multi-stage algorithm. The Hough Transform line is a method that is used in image processing to detect any shape if that shape can be represented in mathematical form. The goal is to piece along a pipeline to detect the line segments within the image, then average/extrapolate them and draw them onto the image for the show.