Lane detection method based on adaptive region of interest

A technology of area of ​​interest and lane detection, applied in the field of computer vision and intelligent vehicle assisted driving, can solve problems such as excessive noise, reduce data processing speed, and fix the area of ​​interest in lanes, achieve accurate detection, ensure accuracy and real-time sexual effect

Active Publication Date: 2016-03-23
山东智瞰深鉴信息科技有限公司
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Problems solved by technology

However, the existing vision-based lane detection methods have the following problems: first, the algorithm is complex, the calculation is large, and the data processing speed is reduced; In some c

Method used

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  • Lane detection method based on adaptive region of interest
  • Lane detection method based on adaptive region of interest
  • Lane detection method based on adaptive region of interest

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Embodiment

[0066] A lane detection method based on an adaptive region of interest, the specific steps comprising:

[0067] (1) Acquire road images; such as figure 1 shown.

[0068] (2) The road image that step (1) is acquired is carried out preprocessing, eliminates non-lane noise, and described non-lane noise refers to the noise produced by other non-lane factors such as sky, tree, road surface, guardrail; Obtain main information as lane Image:

[0069] 1. The improved Sobel edge detection algorithm is used to perform edge detection on the road image obtained in step (1);

[0070] ② Use the optimal threshold binarization method to perform image binarization on the road image obtained in step ①:

[0071] The preprocessed image is as figure 2 shown;

[0072] a. Set the gray value x(m,n) of the road image with a size of M×N at coordinates (m,n) as shown in formula (I):

[0073] x(m,n)=s(m,n)+w(m,n)(I)

[0074] In formula (Ⅰ), M is the number of columns of the road image; N is the n...

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Abstract

The invention relates to a lane detection method based on an adaptive region of interest. The method specifically comprises the following steps: (1) acquiring a road image; (2) pre-processing: detecting edges, binarizing the image, and eliminating isolated points to eliminate non-lane noise; (3) determining the initial position of a lane by single Hough transformation, retaining the upper end point of the initial position of the lane, linearly forecasting the lane in combination with least square fit to obtain an adaptive region of interest changed along with the direction of the lane, and detecting the lane within the obtained adaptive region of interest; and (4) judging that the lane detected in step (3) is straight or curved and outputting video marking the lane. According to the method, the initial position of the lane is determined by single Hough transformation, and a least square method with higher operation speed is adopted in the following linear forecasting process, so that the contradiction between accuracy and instantaneity of a single method is solved, and accuracy and instantaneity are ensured.

Description

technical field [0001] The invention relates to a lane detection method based on an adaptive region of interest, and belongs to the technical fields of computer vision and vehicle intelligent assisted driving. Background technique [0002] With the improvement of traffic conditions, structured roads have become the main environment for vehicles to drive. At the same time, computer vision and vehicle intelligent assisted driving technologies are also developing rapidly, and assisted driving in structured road environments has been widely used. As the key and basic technology of intelligent assisted driving, lane detection has been highly valued by researchers. The vision-based method is the main area that researchers focus on, because the visual data can directly reflect the driving state of the vehicle, and from the perspective of practical application, the camera has the advantages of economy and stability. Currently, vision-based lane detection methods are mainly divided...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V20/588
Inventor 陈辉高建明
Owner 山东智瞰深鉴信息科技有限公司
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