HSV-enhanced Canny-Hough algorithm for robust lane line recognition

Lane line recognition plays an important role in road safety and intelligent transport system. This paper focuses on the key technology of lane line recognition, which is divided into three parts: firstly, the original image is preprocessed, including de-distortion and grey scale, to reduce noise and interference. Then, lane line information extraction is explored in terms of colour features and edge detection. By analysing the RGB and HSV colour modes and their conversion, the colour features are used to improve the accuracy and robustness of recognition. Meanwhile, the lane line edges are extracted by combining the improved Canny algorithm and commonly used edge detection operators, and the methods of threshold segmentation and Hough transform are applied to further describe the geometric features of lane lines. The direction of vehicle motion is determined by calculating the polar coordinate equation and vanishing point coordinates. Finally, this paper verifies the lane line recognition algorithm based on HSV-improved Canny-Hough through simulation experiments, and proves that it is more effective and accurate in lane line recognition compared with traditional methods.