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Rapid lane line detection method based on morphological transform and adaptive threshold

An adaptive threshold and morphology-based technology, applied in the field of image processing, can solve the problems of high reliability, slow speed, and noise sensitivity of Hough transform, achieve reliable detection results, improve reliability and speed, and reduce time

Inactive Publication Date: 2017-05-31
深圳市美好幸福生活安全系统有限公司
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Problems solved by technology

[0005] The least squares method is fast for fitting straight lines but is sensitive to noise, and the Hough transform is highly reliable but slow

Method used

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  • Rapid lane line detection method based on morphological transform and adaptive threshold
  • Rapid lane line detection method based on morphological transform and adaptive threshold
  • Rapid lane line detection method based on morphological transform and adaptive threshold

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Embodiment Construction

[0037] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0038] The experimental platform is win10, the simulation library is opencv 1.0, and the road environment is part of the Beijing-Shanghai Expressway. Firstly, the image is preprocessed, and the lower 1 / 3 part of the image is taken as the area of ​​sensitivity (AOI); since the road line is normally white, the color information in the image can be removed, and the grayscale image can be extracted to obtain the grayscale image as shown in Fig. figure 2 shown.

[0039] Next, threshold the grayscale image and select the 160×80 road surface window in the AOI area to count its mean and variance, suc...

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Abstract

The invention belongs to the field of image processing and discloses a rapid lane line detection method based on morphological transform and adaptive threshold. The problem that the conventional lane line detection algorithm is low in reliability and low in speed is solved. The method comprises the following steps: taking a part, which comprises lane lines, of the lower part of an image as a sensitive area; transforming the colored sensitive area into a gray sensitive area; calculating the threshold based on an adaptive threshold segmentation method of a Gauss distribution model, and performing threshold segmentation on a grayscale image so as to obtain a binary image; performing morphological transform, performing expansive operations on the binary image, performing corrosion operation, and finally making difference between the two images so as to obtain edge information of the images; finally, fitting the lane lines by adopting improved probabilistic Hough transform. The lane line detection method disclosed by the invention is high in reliability and speed, so that the detection time is greatly reduced to 0.016 second, which has a great significance on real-time application.

Description

technical field [0001] The invention belongs to the field of image processing and relates to a fast lane line detection method based on morphological transformation and adaptive threshold. Background technique [0002] At present, the commonly used lane line detection technology is to use the recognition of road lines to realize road detection, using a straight line or curved road model. This method is simple and practical, can adapt to the characteristics of the structured environment of the highway, and has a faster image Processing speed and better real-time performance. After the lane line is segmented through image preprocessing, it is necessary to fit the straight line equation of the lane line in the image. Currently, the commonly used fitting methods include the least square method and Hough transform. [0003] The advantage of using the least squares method for fitting is that it is very fast, and the fitting curve can be calculated by traversing once, but it is ve...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06T7/11G06T7/136
CPCG06T2207/20004G06T2207/10004G06V20/588
Inventor 高振国张开岭张传敬陈丹杰潘永菊郑延景潘伟
Owner 深圳市美好幸福生活安全系统有限公司
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