Lane Line Detection based on Machine Vision

Lane line detection is the key to construct intelligent transportation system. Improving the capability of lane line recognition and curve detection accuracy has been an important part of the research. For the problems of slow speed and low precision of the traditional lane line recognition of yellow lane and the difficulty of Hough transform recognition of curve lane line, a lane line detection method using machine vision is proposed. This method combines white lane line extracted from gray space with yellow lane line extracted from HSV space. Real-time lane detection can be realized by inverse perspective transformation, canny edge detection and sliding window polynomial fitting, and the deviation distance of vehicles can be calculated according to the detection results to carry out deviation warning. The test results indicate that this method is able to accurately identify the straight and curved lane lines of multi-cycle urban roads, and shows good reliability and robustness.