Multi-lane-line detection method

Abstract

The invention discloses a multi-lane-line detection method. Color images of continuous frames are obtained, graying processing is carried out on a current frame of image, and inverse perspective transformation is carried out on a grey-scale map based on a camera parameter and a set grid map size, thereby obtaining a grid map of a road part; according to priori information of a control point, region division is carried out on the grid map by using a Thiessen polygon and image binaryzation processing is carried out on the divided regions; region grouping is carried out based on priori information of lane lines and pixel coordinates of non-zero pixel values is recorded; curve fitting is carried out on a grouped point set by using an improved RANSAC algorithm, a current image control point is predicted by combining a lane line equation and a particle filter algorithm, a final lane line equation is calculated and e perspective transformation is carried out on the lane line equation, and thus a lane line equation in an original image is obtained. With the method, lane line detection precision and robustness are high; and multi-lane-line detection can be completed simultaneously.

Description

technical field [0001] The invention belongs to the field of computer vision and automatic driving, and in particular relates to a multi-lane line detection method. Background technique [0002] In the field of automatic driving, the detection of lane lines is an important link, and it is also a hot issue in the field of automatic driving. However, most of the current lane line detection methods based on computer vision cannot robustly detect lane lines. The main reasons are as follows: illumination, shadows on the road, other obstacles on the road (vehicles, pedestrians, etc.), Other information such as traffic markings will interfere with the detection of lane lines on the road. [0003] At present, there are many multi-lane line detection algorithms based on computer vision. Many journals and conferences related to autonomous driving at home and abroad have listed the lane detection algorithm as a key research field. Scholars at home and abroad have also made a lot of b...

AI Technical Summary

Problems solved by technology

[0006] However, none of the above-mentioned documents can robustly detect multi-lane lines. The main problems are as follows: first, the entire image is processed uniformly in the image preprocessing stage, which greatly affects the interference information such as illumination and shadow; second Second, if the RANSAC algorithm is used to directly fit the point set to the lane line equation, the strip feature of the lane line in the lane line detection task cannot be fully utilized, so that the RANSAC algorithm cannot obtain better results under limited calculations.

Images