Expressway pavement detection method based on lane lines

Abstract

The invention discloses an expressway pavement detection method based on lane lines. The method includes the steps: continuously acquiring at least one video frame image of a video file and acquiringa target video frame image according to at least one video frame image; detecting the edge of the target video frame image to obtain an edge image comprising road edge pixel points; scanning the edgeimage to obtain a road region, horizontally dividing the road region to obtain sub-regions, detecting each sub-region by probability Hough transformation to obtain road edge line segments, solving vanishing points according to all the edge line segments in the sub-region at the top end of the road region, and determining an intermediate control point of each sub-region not at the bottom end of theroad region and a boundary point of the sub-region at the bottommost end of the road region according to the fact whether a straight line with a maximum slope and a straight line with a minimum slopein each sub-region have an intersection point or not; drawing left and right lane edge lines according to the intermediate control point, the boundary point and the vanishing points. By the method, adaptability to bend scenes is improved.

Description

technical field [0001] The invention relates to the field of road surface detection, in particular to a lane line-based expressway road surface detection method. Background technique [0002] Intelligent video surveillance of expressways usually only focuses on the road surface area in the picture, but the surveillance images often contain backgrounds such as sky, trees, buildings, etc., which undoubtedly increases the computational overhead of the monitoring algorithm. At the same time, non-road surface areas are often accompanied by shaking leaves, light These interference factors also affect the accuracy of monitoring. Therefore, it is necessary to extract the highway road surface area in the video image as the preprocessing of video surveillance, filter out the background irrelevant to the road surface, reduce the redundant data in the image, improve the calculation speed, and avoid image information in irrelevant areas from being used for later image processing. interf...

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Problems solved by technology

[0004] Based on the above ideas, Wang et al. published "Image and Vision computing" in "Image and Vision computing" in 2004. It is assumed that the roads in the monitoring scene are parallel, and the B-snake curve is used to fit the lane line. Compared with the spline Interpolation curve, this method makes the generated curve approach as much as possible instead of passing through the interpolation point, the fitted curve is flexible and smooth but requires multiple Hough transforms, which is not conducive to controlling the calculation amount of the algorithm; Jung et al. in 2005 in "Imageand "Lane Following and Lane Departure Using a Linear-parabolic Model" was published on Vision Computing, which uses sub-regional lane line fitting, the nearby road area uses a linear model to fit the lane line, and the far road area uses a parabolic model to fit Lane lines, but the far and near areas in this algorithm are divided in advance, the adaptability is poor, and the scope of use is limited; Lipski et al published "A Fast and Robust Approach" in "In Proceedings of IEEESouthwest Symposium on Image Analysis and Interpretation" in 2008 to Lane Marking Detection and Lane Tracking》calculate the local histogram of the road surface image, and extract the characteristic information such as the color of the road surface image and the road direction. Shadow coverage and the decrease in the clarity of lane lines will affect the detection results; Lee et al published "Effectivelane detection and tracking method using statistical modeling of color and lane" in "In Proceedings of 4th International Conference on Computer Sciences and Convergence Information Technology" in 2009 edge-orientation"Using lane line color and edge information to obtain road lane line pixels and calculate the histogram of edge information and HSV space color information, use Bayesian criteria to classify each pixel in the image, extract lane line pixels, and Fu transformation for fitting; Kong et al published "General Road Detect" in "IEEE Trans on Image Processing" in 2010 ion from A Signal Image》Use Gabor filtering to calculate the local texture of pixels, obtain edge information, and perform Hough transform to find and locate the road ROI area in the image. Although this method is less affected by noise, it is computationally complex too high

[0005] Among the above methods, the model-based method only needs to solve fewer model parameters, has a small amount of calculation, and has strong robustness to noise, but the accuracy of extracting the road surface area depends on the selection of the model and the solution method. The method of feature extraction is directly related to the selection of road surface features, and the expressway will have curves, and the lane line fitting of the entire picture directly affects the accuracy of left and right lane division in this scene

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