A road detection method and device based on disparity map

Abstract

The present application provides a road detection method and device based on a disparity map, which relates to the technical field of image processing. The method includes: determining a sparse disparity map and a V disparity map of an image to be detected; obtaining the V disparity according to a preset pixel threshold The number of effective pixels in the columns of the graph and the row range corresponding to the effective pixels determine the row to which the road surface belongs in the V disparity map; according to the row to which the road surface belongs in the V disparity map, in the sparse disparity The road surface parallax point is determined in the figure, wherein the parallax value of the road surface parallax point is the parallax value corresponding to the column. By applying this method, the accuracy and efficiency of determining road parallax points in the parallax image can be improved, and at the same time, the method can be applied to scenes with rough and uneven road surfaces.

Description

technical field [0001] The present application relates to the technical field of image processing, in particular to a road detection method and device based on a disparity map. Background technique [0002] In recent years, the obstacle detection method based on binocular stereo vision (Binocular Stereo Vision) technology has become a research hotspot in the field of automobile assisted driving. Especially important for obstacle detection results. In the prior art, a sparse disparity map is first generated based on the road image collected by a binocular camera, and then a V disparity map is generated based on the sparse disparity map, and one or more straight lines are detected in the V disparity map by using the Hough line detection method To fit the straight line equation of the road surface in the V disparity map, and then find the disparity points satisfying the straight line equation in the sparse disparity map. The road pavement is deleted. [0003] However, the ab...

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Problems solved by technology

[0003] However, the above method of deleting the road surface is only suitable for ideally flat roads, and the roads in actual scenarios are likely to be undulating, such as from flat roads to downhill roads, from uphill roads to flat roads, and even other more complex roads. For this type of road conditions, it is difficult to use a straight line for fitting because the transition zone of different road sections usually manifests as a circular arc transition. Therefore, if a Hough straight line is used to detect the parallax point of the road surface, it is easy to cause The obstacle parallax is deleted by mistake in the road section, or the distant road surface parallax cannot be deleted, resulting in inaccurate obstacle detection results in the later stage

[0004] Based on this, it is proposed in the prior art to segment the road according to the distance and the undulations of the road surface, and each section corresponds to a Hough straight line to detect the road surface parallax point on the section of the road. However, the more times the Hough line fits, the longer it will take, resulting in the consumption of computing resources; at the same time, due to the complex and unpredictable road conditions in practical applications, it is inevitable to appear in the Undulations appear on a pre-divided section of the road; in addition, in the case of less parallax on the road surface, if the road is divided into multiple sections for detection, it may not be possible to fit the straight line equation of the road surface

It can be seen that the existing technology is not suitable for the actual scene of uneven roads, and it is easy to cause the accuracy of determining the parallax of the road surface to be low, which will affect the result of obstacle detection in the later stage.

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