CN109670443B - Improved Hough transformation road line detection method based on superposition constraint - Google Patents
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Abstract
The invention relates to an improved Hough transformation road line detection method based on superposition constraint, which comprises the following steps of S1: acquiring a road image; step S2: preprocessing a road image; and step S3: detecting and extracting the road line through the improved Hough transformation of the superposition constraint; and step S4: and comparing the detection result of the step S3 with a threshold value to finally obtain an output result. The method can still accurately detect the road line information of the current driving in real time under the conditions of variable environmental factors and complex road conditions, effectively improves the detection rate and the real-time property, and has good robustness.
Description
Technical Field
The invention relates to the field of battery testing, in particular to an improved Hough transformation road line detection method based on superposition constraint.
Background
With the improvement of living standard and the continuous progress of science and technology, the intelligent driving technology gradually receives the extensive research and attention of researchers. An Advanced Driver Assistance System (ADAS) is a branch of intelligent driving technology, and the extraction of road lines is an important component of the intelligent driving System, and is also one of the key points for researching automatic driving, mainly by detecting the road line information of the video original image obtained by a camera in a vehicle. However, the detection of the road route is greatly influenced by weather influence factors and road multiple environments, such as: visual angle shielding, road shadow, lines and marks on the road, vehicle pressing lines on the periphery and the like, so that the road line is not easy to extract and is easy to detect and fail. The Hough transform algorithm is a technology for detecting road lines, but the time for averagely processing each frame is long due to the classical Hough transform, and the road lines are difficult to accurately extract due to the factors of variable environment and complex roads.
Disclosure of Invention
In view of this, the present invention provides an improved Hough transform road line detection method based on superposition constraint, which can accurately detect the current driving road line information in real time under the conditions of variable environmental factors and complex road conditions, thereby effectively improving the detection rate and real-time performance and having good robustness.
The invention is realized by adopting the following scheme: a road line detection method based on superposition constraint and improved Hough transformation comprises the following steps:
step S1: acquiring a road image;
step S2: preprocessing a road image;
and step S3: detecting and extracting the road line through the improved Hough transformation of the superposition constraint;
and step S4: and comparing the detection result of the step S3 with a threshold value to finally obtain an output result.
Further, step S2 specifically includes the following steps:
step S21: dividing a Dynamic region of interest (ROI) of the RGB image;
step S22: converting the RGB road image processed in the step S21 into a color space;
step S23: performing binarization processing on the image processed in the step S22;
step S24: denoising the binarized image obtained after the processing of the step S23 by using a morphological filtering algorithm; the morphological filtering algorithm is to perform operation processing on the obtained road image after corrosion; the purpose of corrosion is to remove noise and useless information in a road image; the purpose of the expansion operation is to compensate for excessive erosion of the road line;
step S25: and (5) performing edge extraction processing on the denoised binary image in the step (S24). The edge extraction process uses Canny edge detection.
Preferably, step S1 acquires the road image through a camera.
Further, in step S21, the dynamic region of interest is divided, that is, the initial road line detection region is set to be rectangular.
Further, in step S22, the color space conversion is performed from RGB to YCbCr, and yellow and white information in the lane is extracted.
Further, step S23 specifically includes: and (4) carrying out threshold value comparison after the Y component is restrained, setting the reserved road information larger than the threshold value to be 255, and otherwise, abandoning the road information and setting the road information to be 0.
Further, step S3 specifically includes the following steps:
step S31: carrying out angle constraint screening on the road image preprocessed in the step S2;
step S32: performing slope constraint screening on the road image screened in the step S31;
step S33: and (4) screening left and right road lines of the road image screened in the step (S32).
Wherein, the formula of the straight line in the rectangular coordinate system is as follows:
y＝kx+b＝(-cosγ/sinγ)·x+(r/sinγ)；
where y represents the ordinate, x represents the abscissa, k represents the slope of the line, b represents the intercept of the line, and γ represents the angle between the perpendicular to the line through the origin and the positive direction of the x-axis.
The formula of the straight line in the polar coordinate system is as follows:
r＝x·cosγ+y·sinγ；
where r represents the distance of the length from the origin of the position of the coordinates to the straight line (requiring a length value less than its diagonal),
representing the height and width of the region of interest image; and represents the angle (gamma. Epsilon. [0,180 deg. ] between the perpendicular line passing through the origin and the straight line and the positive direction of the x-axis).
Further, according to the imaging principle, the road route is generally a few lines which are far, small and near and large on the image, and vanishing points exist, so that detection of invalid straight lines can be avoided through angle interval limitation, and meanwhile, the calculation amount is reduced to improve the real-time performance. Therefore, an angle constraint is performed on the image when the straight line is screened for the first time. Step S31 specifically includes: fitting an angle theta by Hough transformation to avoid detection of invalid straight lines; wherein theta is an included angle between the left and right path lines and the central line; and let θ e [20 °,84 ° ] [96 °,160 ° ] when θ < 90 °, the straight line is the road line of the left road, and when 90 ° < θ < 180 °, the straight line is the road line of the right road.
Furthermore, since the difference between 84 ° and 90 ° or between 90 ° and 96 ° is very small, in order to prevent errors in fitting the function or other lines with similar angles in the road due to insufficient detection points, a slope constraint is screened, mainly to screen out the influence of horizontal lines, vertical lines, road sign lines, or the like on the road line fitting, and to retain useful road line information. The slope and angle of the road are calculated according to the following formula:
k＝(y 2 -y 1 )/(x 2 -x 1 )；
θ＝arctank -1 ＝arctan((x 2 -x 1 )/(y 2 -y 1 ))；
wherein (x) 1 ，y 1 ) And (x) 2 ，y 2 ) Two points on a straight line. Since the road line is divided into left and right road lines, the slope is positive or negative. Therefore, traces in this range are only preserved if the slope satisfies 0.1 < | k | < 2.7. Step S32 thus specifically is: if the slope is within the range of 0.1 < | k | < 2.7, the road route is saved.
And further, finally, screening the road lines on the left side and the right side. In order to prevent the situation that the road lines are not accurately fitted during fitting, the outermost road lines on the left side and the outermost road lines on the right side are further screened out on the basis, and only the road lines meeting the following formula can be stored. Step S33 specifically includes: screening the road lines on the outermost sides in the left and right sides, wherein the road lines on the outermost sides in the left and right sides satisfy:
D dis ＝max|(N/2-(x 4 +1/k 3,4 )·(M-y 4 ))|；
wherein M represents the width of the image, N represents the height of the image, (x) 4 ,y 4 ) Denotes a pixel coordinate point, k 3,4 Indicates the slope, D, at which the pixel coordinate point is located dis When the representation value is maximum, the representation value is the outer side lines on the left side and the right side of the road where the vehicle is located.
Further, step S4 further includes: and presenting the output result on the video stream acquired by the initial camera.
Compared with the prior art, the invention has the following beneficial effects: the invention utilizes the improved Hough transform road route detection of superposition constraint, namely, the angle constraint screening is firstly carried out on the route, and then the slope constraint screening and the left and right side road route constraint screening are carried out on the route, thereby solving the problem that the road line is difficult to accurately extract due to the variable environmental factors and the complex road condition. In rainy days, tunnel environment and at night, road information contains traffic sign lines, virtual and real road lines, road damage and road pressing lines of vehicles beside the road, and the road line can be efficiently detected, the real-time performance is improved, and the road detection method has good robustness.
Drawings
FIG. 1 is a general flow chart of an algorithm according to an embodiment of the present invention.
Fig. 2 is a corresponding relationship between a rectangular coordinate system and a polar coordinate system according to an embodiment of the invention.
Fig. 3 is a flowchart of detecting an improved Hough transformation road line with overlay constraint according to an embodiment of the present invention.
FIG. 4 is a diagram showing exemplary results of output images before and after modification according to an embodiment of the present invention; wherein, (a) is the graph of the output result of the traditional Hough detection, and (b) is the graph of the output result of the method of the invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the embodiment provides a road line detection method based on an improved Hough transform of superposition constraint, which includes the following steps:
step S1: acquiring a road image;
step S2: preprocessing a road image;
and step S3: detecting and extracting the road lines through the improved Hough transformation of the superposition constraint;
and step S4: and comparing the detection result of the step S3 with a threshold value to finally obtain an output result.
In this embodiment, the step S2 specifically includes the following steps:
step S21: dividing a Dynamic region of interest (ROI) of the RGB image;
step S22: converting the RGB road image processed in the step S21 into a color space;
step S23: performing binarization processing on the image processed in the step S22;
step S24: denoising the binarized image obtained after the processing of the step S23 by using a morphological filtering algorithm; the morphological filtering algorithm is to carry out corrosion-first expansion operation processing on the obtained road image; the purpose of corrosion is to remove noise and useless information in a road image; the purpose of the expansion operation is to compensate for excessive erosion of the road line;
step S25: and (5) performing edge extraction processing on the denoised binary image in the step (S24). The edge extraction process uses Canny edge detection.
Preferably, step S1 acquires the road image through a camera.
In this embodiment, in step S21, the dynamic region of interest is divided, that is, the initial road line detection region is set to be a rectangle, that is, the ROI is a rectangle.
In step S22 in this embodiment, the color space conversion is performed from RGB to YCbCr, and yellow and white information in the lane is extracted.
In this embodiment, step S23 specifically includes: and (4) carrying out threshold value comparison after the Y component is restrained, setting the reserved road information larger than the threshold value to be 255, and otherwise, abandoning the road information and setting the road information to be 0.
In this embodiment, as shown in fig. 3, step S3 specifically includes the following steps:
step S31: carrying out angle constraint screening on the road image preprocessed in the step S2;
step S32: carrying out slope constraint screening on the road image screened in the step S31;
step S33: and (4) screening left and right road lines of the road image screened in the step (S32).
Fig. 2 shows the correspondence between the rectangular coordinate system and the polar coordinate system. The basic principle of Hough transformation is to map curves or straight lines with the same shape characteristics in one space to points in another space by using the transformation relation existing between two geometric coordinate systems, and finally accumulate to form a peak value, and finally convert the problem of detecting any shape in an image into the problem of analyzing the peak value points.
The formula of the straight line in the rectangular coordinate system is as follows:
y＝kx+b＝(-cosγ/sinγ)·x+(r/sinγ)；
where y represents the ordinate, x represents the abscissa, k represents the slope of the line, b represents the intercept of the line, and γ represents the angle between the perpendicular to the line passing through the origin and the positive direction of the x-axis.
The formula of the straight line in the polar coordinate system is as follows:
r＝x·cosγ+y·sinγ；
where r represents the distance of the length from the origin of the position of the coordinates to the straight line (requiring a length value less than its diagonal),
representing the height and width of the region of interest image; and represents the angle (gamma. Epsilon. [0,180 deg. ] between the perpendicular line passing through the origin and the straight line and the positive direction of the x-axis).
In this embodiment, according to the imaging principle, the road line is generally a few lines with small distance and large distance on the image, and there is a vanishing point, so that the detection of some invalid straight lines can be avoided by the limitation of the angle interval, and at the same time, the calculation amount is reduced to improve the real-time performance. Therefore, an angle constraint is performed on the image when the straight line is screened for the first time. Step S31 specifically includes: fitting an angle theta by Hough transformation to avoid detection of invalid straight lines; wherein theta is an included angle between the left and right road routes and the central line; and let θ e [20 °,84 ° ] [96 °,160 ° ] when θ < 90 °, the straight line is the road line of the left road, and when 90 ° < θ < 180 °, the straight line is the road line of the right road.
In this embodiment, since the difference between 84 ° and 90 ° or between 90 ° and 96 ° is very small, in order to prevent error in fitting the function or fitting other lines with similar angles in the road due to insufficient detection points, a slope constraint is screened, mainly to screen out the influence of horizontal lines, vertical lines, road sign lines, or the like on the road line fitting, and to retain useful road line information. The slope and angle of the road are calculated according to the following formula:
k＝(y 2 -y 1 )/(x 2 -x 1 )；
θ＝arctank -1 ＝arctan((x 2 -x 1 )/(y 2 -y 1 ))；
wherein (x) 1 ，y 1 ) And (x) 2 ，y 2 ) Two points on a straight line. Since the road line is divided into left and right road lines, the slope may involve positive and negative. Therefore, traces in this range are only preserved if the slope satisfies 0.1 < | k | < 2.7. Step S32 is thus specifically: if the slope is within the range of 0.1 < | k | < 2.7, the road route is saved.
In this embodiment, the left and right road lines are finally screened. In order to prevent the situation that the road lines are not accurately fitted during fitting, the outermost road lines in the left side and the right side are further screened out on the basis, and only the road lines meeting the following formula can be stored. Step S33 specifically includes: screening the road lines on the outermost sides in the left and right sides, wherein the road lines on the outermost sides in the left and right sides meet the following requirements:
D dis ＝max|(N/2-(x 4 +1/k 3,4 )·(M-y 4 ))|；
wherein M represents the width of the image, N represents the height of the image, (x) 4 ,y 4 ) Representing pixel coordinate points, k 3,4 Indicates the slope, D, at which the pixel coordinate point is located dis When the representation value is maximum, the representation value is the outer side lines on the left side and the right side of the road line where the vehicle is located.
In this embodiment, step S4 further includes: and presenting the output result on the video stream acquired by the initial camera.
Fig. 4 is a diagram showing an example result of an output image before and after improvement according to an embodiment of the present invention, wherein (a) is a diagram showing an output result of a conventional Hough test, and (b) is a diagram showing an output result of a method according to the present invention. It can be seen that the road line identified by the lock is more accurate by adopting the method of the embodiment.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (6)
1. A road line detection method based on superposition constraint and improved Hough transformation is characterized in that: the method comprises the following steps:
step S1: acquiring a road image;
step S2: preprocessing a road image;
and step S3: detecting and extracting the road line through the improved Hough transformation of the superposition constraint;
and step S4: comparing the detection result of the step S3 with a threshold value to finally obtain an output result;
the step S3 specifically includes the following steps:
step S31: carrying out angle constraint screening on the road image preprocessed in the step S2;
step S32: performing slope constraint screening on the road image screened in the step S31;
step S33: screening the left and right road lines of the road image screened in the step S32;
step S31 specifically includes: fitting an angle theta by Hough transformation to avoid detection of invalid straight lines; wherein theta is an included angle between the left and right path lines and the central line; and is provided with
When theta is less than 90 degrees, the straight line is the road line of the left road, and when theta is less than 90 degrees and less than 180 degrees, the straight line is the road line of the right road;
step S32 specifically includes: only the road route with the slope in the range of 0.1 < | k | < 2.7 is stored;
step S33 specifically includes: screening the road lines on the outermost sides in the left and right sides, wherein the road lines on the outermost sides in the left and right sides meet the following requirements:
D dis ＝max|(N/2-(x 4 +1/k 3,4 )·(M-y 4 ))|；
wherein M represents the width of the image, N represents the height of the image, (x) 4 ,y 4 ) Representing pixel coordinate points, k 3,4 Indicates the slope, D, at which the pixel coordinate point lies dis When the representation value is maximum, the representation value is the outer side lines on the left side and the right side of the road line where the vehicle is located.
2. The method for detecting the road line based on the improved Hough transform of the superposition constraint according to claim 1, wherein the method comprises the following steps: the step S2 specifically includes the following steps:
step S21: dividing a dynamic region of interest of the RGB image;
step S22: converting the RGB road image processed in the step S21 into a color space;
step S23: performing binarization processing on the image processed in the step S22;
step S24: denoising the binarized image obtained after the processing of the step S23 by using a morphological filtering algorithm;
step S25: and (5) performing edge extraction processing on the denoised binary image in the step (S24).
3. The method according to claim 2, wherein the method comprises the following steps: in step S21, the dynamic region of interest is divided, that is, the initial road line detection region is set to be a rectangle.
4. The method for detecting the road line based on the improved Hough transform of the superposition constraint according to claim 2, wherein the method comprises the following steps: in step S22, the color space conversion is performed from RGB to YCbCr, and yellow and white information in the lane is extracted.
5. The method according to claim 2, wherein the method comprises the following steps: step S23 specifically includes: and (4) carrying out threshold value comparison after the Y component is restrained, setting the reserved road information larger than the threshold value to be 255, and otherwise, abandoning the road information and setting the road information to be 0.
6. The method for detecting the road line based on the improved Hough transform of the superposition constraint according to claim 1, wherein the method comprises the following steps: step S4 further includes: and presenting the output result on the video stream acquired by the initial camera.
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