Multiple lane line detecting method based on isometric peripheral point matching

Abstract

The invention discloses a multiple lane line detecting method based on isometric peripheral point matching. The method comprises the following steps: collecting an image of a road in front of an automobile, conducting inverse perspective projection change on the collected image of the road in front of the automobile by utilizing the interrelation of world coordinate, camera coordinate and image coordinate to obtain an aerial view of the image of the road, conducting edge detection on the aerial view, conducting pretreatment on the detected edge view, conducting local area matching on all edge points of the pretreated edge view, reserving successfully matched edge point pairs, obtaining a feature point diagram through the edge point pairs, conducting partitioning on diagram according to distribution of the feature points, extracting all feature points of one lane line I, isolating feature point areas which belong to other lane lines II, and taking the feature point areas as the candidate area of the lane line I. According to the method, the technical problems of multiple lane line detection, lane line positioning and bending, and solid lane line and dotted lane line discrimination in the conventional method can be effectively solved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and more specifically relates to a multi-lane line detection method based on equidistant edge point matching. Background technique [0002] With the popularization of family cars, traffic safety issues have been paid more and more attention by people. According to statistics, in the world, the number of deaths caused by road traffic accidents is 500,000 people every year, which has become a major threat to life safety. [0003] In order to improve road safety and avoid traffic accidents, many research institutions and companies have conducted a lot of research on intelligent assisted driving, hoping to provide some auxiliary information before accidents to ensure traffic safety. The intelligent assisted driving system usually uses GPS, laser, radar, computer vision and other methods to cooperate with each other. Among them, the intelligent assistance method using computer vision has att...

AI Technical Summary

Problems solved by technology

However, most of the current lane line detection methods only detect the lane lines on both sides of the lane where the car is located. Although this reduces the difficulty of lane line detection, it loses a lot of important information.

[0007] 2. Unable to quickly and accurately locate curved lane lines

Among the existing methods, some use Hough transform to detect lane lines, which can only detect straight lane lines, and can only guarantee the detection results of a short distance ahead in the case of curved lane lines; some use parabolic models to detect curved lane lines. Lane fitting, although the parabolic model can fit simple turning situations and the calculation speed is relatively fast, but it is prone to deviation in complex road scenarios such as lane splitting and roundabouts; while using a more complex model, it is faced with The amount of calculation increases and the problem that cannot be detected in real time

[0008] 3. It is impossible to distinguish between real lane lines and virtual lane lines

In the actual driving process of a car, real lane lines and virtual lane lines have different meanings. Distinguishing them will help improve the results of lane line detection. However, due to the discontinuous nature of virtual lane lines, it is sometimes difficult to detect them. Detected, and there are few ways to distinguish them

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