Method for detecting multi-lane line on basis of random sample consensus (RANSAC) algorithm

A multi-lane and line detection technology, applied in computing, computer components, instruments, etc., can solve the problem of unresolved lane lines, achieve the effect of solving the problem of curved lanes and strong stability

Inactive Publication Date: 2012-10-10
WUHAN UNIV
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Problems solved by technology

However, the problem of how to extract curved la

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  • Method for detecting multi-lane line on basis of random sample consensus (RANSAC) algorithm
  • Method for detecting multi-lane line on basis of random sample consensus (RANSAC) algorithm
  • Method for detecting multi-lane line on basis of random sample consensus (RANSAC) algorithm

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Embodiment

[0045] The real-time urban multi-lane line detection method based on the RANSAC algorithm, as shown in the figure, includes the following steps:

[0046] Step 1: Collect the original image of the road condition in front of the vehicle. During the driving process of the smart car, the original image of the road conditions in front of the vehicle is collected through the camera installed on the vehicle.

[0047] Step 2: Obtain an inverse perspective map (Inverse Perspective Mapping, IPM) from the original image. According to the internal parameters of the camera (focal length and optical center) and external parameters (pitch angle, horizontal angle and the height of the camera from the ground), the world coordinates are obtained. The center is the optical center of the camera, a camera coordinate, and a conversion matrix of image coordinates. For any point in the picture, after matrix transformation, it can be transformed into a point under the corresponding road coordinates. ...

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Abstract

The invention relates to a method for detecting a multi-lane line on basis of a random sample consensus (RANSAC) algorithm, which comprises the following steps: step 1, the initial image of the road condition in front of a car is acquired, i.e. during the travelling process of the smart car, acquiring the initial image of the road condition in front of the car is acquired through a camera which is installed on the car; step 2, the initial image which is obtained in the step 1 is subjected to inverse perspective mapping for obtaining an inverse perspective drawing; step 3, a two-dimensional Gaussian core is utilized to carry out image preprocessing on the inverse perspective drawing in the step 2; step 4, Hough transformation is carried out on the preprocessed image, so a lane candidate line is obtained, the quick RANSAC verification is carried out on the lane candidate line through utilizing the Hough transformation, and the revised lane candidate line is obtained; and step 5, the revised lane candidate line which is obtained in the step 4 is post-processed. Consequently, the method has the following advantages that the method has strong stability, identifies the multi-lane line and can better solve the problem of the crooked lane.

Description

technical field [0001] The invention relates to a multi-lane line detection method, in particular to a multi-lane line detection method based on RANSAC algorithm. Background technique [0002] One of the most important areas of research and development of smart vehicles is road safety, which has received more and more attention at present. Traffic accidents have become "the world's number one hazard", and China is one of the countries with the largest number of traffic accident deaths in the world. Since the end of the 1980s, the annual death toll in China's traffic accidents has exceeded 50,000 for the first time, and China (excluding Hong Kong, Macao and Taiwan) has 500,000 traffic accidents every year, and the death toll due to traffic accidents has exceeded 100,000. Yu Nian ranks first in the world. Autonomous driving is an inevitable trend in the development of automobile technology, and has become a hot research topic in the field of automobiles in various countries....

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Application Information

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IPC IPC(8): G06K9/00
Inventor 肖进胜宋晓李清泉沈三明易本顺朱神添李必军李明
Owner WUHAN UNIV
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