Advanced Lane Detection for Autonomous Vehicles using Computer Vision techniques

The distortion matrix was used to un-distort a calibration image and provides a demonstration that the calibration is correct. An example shown here in Fig 2, shows the before/after results after applying calibration to un-distort the chessboard image. <img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*eW70zrINQURQYsxayorvGQ.png" style="height:212px; width:700px" /> Fig 2. Before and after results of un-distorting a chessboard image (Source: Udacity) 2. Apply a distortion correction to raw images. The calibration data for the camera that was collected in step 1 can be applied for raw images to apply distortion correction. An example image is shown here in Fig 3. It may be harder to see the effects of applying distortion correction on raw images compared to a chessboard image, but if you look closer at right of the image for comparison, this effect becomes more obvious when you look at the white car that has been slightly cropped along with the trees when the distortion correction was applied. <a href="https://towardsdatascience.com/advanced-lane-detection-for-autonomous-vehicles-using-computer-vision-techniques-f229e4245e41">Visit Now</a>