Lane detection and tracking based on improved Hough transform and least-squares method

Abstract

Lane detection and tracking play important roles in lane departure warning system (LDWS). In order to improve the real-time performance and obtain better lane detection results, an improved algorithm of lane detection and tracking based on combination of improved Hough transform and least-squares fitting method is proposed in this paper. In the image pre-processing stage, firstly a multi-gradient Sobel operator is used to obtain the edge map of road images, secondly adaptive Otsu algorithm is used to obtain binary image, and in order to meet the precision requirements of single pixel, fast parallel thinning algorithm is used to get the skeleton map of binary image. And then, lane lines are initially detected by using polar angle constraint Hough transform, which has narrowed the scope of searching. At last, during the tracking phase, based on the detection result of the previous image frame, a dynamic region of interest (ROI) is set up, and within the predicted dynamic ROI, least-squares fitting method is used to fit the lane line, which has greatly reduced the algorithm calculation. And also a failure judgment module is added in this paper to improve the detection reliability. When the least-squares fitting method is failed, the polar angle constraint Hough transform is restarted for initial detection, which has achieved a coordination of Hough transform and least-squares fitting method. The algorithm in this paper takes into account the robustness of Hough transform and the real-time performance of least-squares fitting method, and sets up a dynamic ROI for lane detection. Experimental results show that it has a good performance of lane recognition, and the average time to complete the preprocessing and lane recognition of one road map is less than 25ms, which has proved that the algorithm has good real-time performance and strong robustness.