Abstract:
Self-driving cars may need to recognize lane lines. OpenCV is used to recognize road lane lines in this project. OpenCV uses input photos to determine lane lines and produce lane illustrations. OpenCV tools like color selection, region of interest selection, grey scaling, Gaussian smoothing, Canny Edge Detection, and Hough Transform line detection are used.
A colour detection method finds pixels in a picture that match a color or range. Region of interest selection lets you select a rectangle, crop it, and display the cropped image. Grey scaling converts RGB, CMYK, HSV, and other color schemes to shades of grey.
Gaussian Blur convolves the image using a mathematical filter instead of a box filter. Gaussian filters may be low-pass filters that remove high-frequency elements. Canny Edge Detection detects picture edges. It processes grayscale images using a multi-stage method.
Image processing uses the Hough Transform line to recognize any mathematically represented shape. A pipeline is used to detect image line segments, average/extrapolate them, and draw them onto the image for the display.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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