Road edge detection method based on heuristic probability Hough transformation

An edge detection and edge technology, applied in character and pattern recognition, image data processing, instruments, etc., can solve problems such as wrong road detection results

Active Publication Date: 2013-12-25
NANTONG QIWU AGRI PROD CO LTD
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AI Technical Summary

Problems solved by technology

[0004] In the mapping stage from the image space to the parameter space, the traditional PHT method randomly selects candidate edge points from the image space without using the previous heuristic information (detection results such as the angle and position of the road boundary); the RHT method uses the previous road detection The information divides the target search area OSA, but makes the edge points outside the OSA have no chance to participate in the mapping. When the road boundary is outside the OSA due to sudden changes, it may lead to wrong road detection results.

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  • Road edge detection method based on heuristic probability Hough transformation
  • Road edge detection method based on heuristic probability Hough transformation
  • Road edge detection method based on heuristic probability Hough transformation

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Embodiment 1

[0050] A road weighted Canny edge detection method, firstly uses the least squares method to perform straight line fitting for each edge point subset, and obtains the axis of the point set. Suppose the equation of the line is L: , then the distance from any point (x, y) in the subset to the straight line is , the vertical coordinates . Suppose two points in the subset point of foot on line L are the two endpoints of all vertical feet on the axis, and it can be easily shown that established. so, arrive The map is a compressed map, which means that the line segment For any vertical foot point on , at least one corresponding point can be found in the subset of edge points. In the subset of edge points, the point with the smallest distance is selected as the corresponding point, and the other points with the same vertical foot are non-corresponding points.

[0051] Use formula (1) to assign a weight to any point i in the edge point subset, where S is the siz...

Embodiment 2

[0058] A heuristic probability Hough transform method for weighted Canny edge images. According to the historical identification results of road boundary lines L, the entire image space is divided into regions of interest and regions of non-interest, and edge points distributed in different regions have different probabilities. value. as attached figure 1 , 2 As shown, the shaded area is the ROI area. The edge points in this area have a higher probability of P2, so the probability of being randomly selected is higher, while the probability of edge points in other areas is P1, and the probability of being selected is higher. lower. This means that longer edge segments within the ROI will have a good chance of being selected first, rather than the longest straight line in image space. Although the candidate edge points in the non-ROI area have a low probability of being selected, they do not deprive the chance of random mapping. When the boundary of the road is suddenly locat...

Embodiment 3

[0067] A method for extracting a road weighted Canny edge bi-polyline model. The bi-polyline model divides the road boundary into two segments: the near segment and the far segment, which represent the direction and trend of the near road and the far road, respectively. The extraction method of this model is as follows:

[0068] 1. Extract the weighted Canny edge from the original image, and filter according to the weight of the image edge to generate the edge family of candidate road boundaries.

[0069] 2. Use the least squares method to estimate the main direction of the candidate edge family, and project all edge points to the main axis generated by the least squares method, and select the center position O of the edge family on the main axis according to the projection range.

[0070] 3. The line passing through point O and perpendicular to the main axis divides the edge family into two parts. First, perform Hough transform on the near edge family part, and then define th...

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Abstract

The invention relates to a road edge detection method based on heuristic probability Hough transformation. An Otsu method serving as a self-adapting threshold search method has good robustness on non-structured road region segmentation under a complicated environment. However, the Otsu method can only be used for obtaining an approximate segmentation result of a road region and a non-road region while being incapable of obtaining an accurate road boundary. The method provided by the invention comprises the following steps: uniformly numbering mutually communicated edge pixels to form a plurality of subsets of a Canny edge at a double-threshold detection stage; and firstly carrying out linear fitting on the subset of each edge point through a least square method to figure out the axial line of the point set. The method provided by the invention is applied to road edge detection.

Description

Technical field: [0001] The invention relates to a road edge detection method based on heuristic probability Hough transform. Background technique: [0002] As an adaptive threshold search method, the Otsu method has good robustness for segmentation of unstructured road areas in complex environments. However, the Otsu method itself can only obtain approximate segmentation results of road and non-road areas, but cannot obtain precise road boundaries. How to make the smart vehicle know if there is a passable area ahead, how to control the direction of the smart vehicle to drive along the road, etc., all rely on the detection of the roadside. In outdoor driving, road conditions and ambient lighting have a great influence on the results of roadside detection. Whether the edge detection algorithm can be disturbed by these factors as little as possible. On the other hand, in order to control the driving of intelligent mobile vehicles in real time, the speed of the algorithm is a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/46
Inventor 王燕清石朝侠陈德运孙晓君孙广路李扬李松唐远新
Owner NANTONG QIWU AGRI PROD CO LTD
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