CN117351433A - Computer vision-based glue-cured mortar plumpness monitoring system - Google Patents
Computer vision-based glue-cured mortar plumpness monitoring system Download PDFInfo
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- 239000004570 mortar (masonry) Substances 0.000 title claims abstract description 308
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- 239000004571 lime Substances 0.000 description 1
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Abstract
The invention relates to the technical field of image region segmentation, in particular to a computer vision-based glue-cured mortar fullness monitoring system. The system comprises: acquiring the gray level difference of a pixel point in a brick surface gray level image, setting a mortar clustering point and a brick surface clustering point in the brick surface gray level image, and acquiring the mortar possibility of the pixel point according to the position of the pixel point in the brick surface gray level image, the difference between the gray level difference of the pixel point and a reference pixel point thereof and the difference between the gray level difference of the pixel point and the mortar clustering point; and acquiring an actual mortar area based on the mortar possibility, and monitoring the fullness of the glued mortar according to the area of the actual mortar area. According to the invention, whether the pixel points are positioned in the mortar area is judged by the mortar possibility of the pixel points, so that the actual mortar area is more accurate, and the accuracy of monitoring the fullness of the glue-cured mortar is improved.
Description
Technical Field
The invention relates to the technical field of image region segmentation, in particular to a computer vision-based glue-cured mortar fullness monitoring system.
Background
The glue-fixed mortar is a special type of building material, can be used for bricking and tile installation, and plays a role in brick adhesion in the bricking process. Mortar plumpness is the effective bonding degree of mortar and brick when the brick is built, and the mortar plumpness has great influence on the strength, durability and overall attractive degree of the masonry, so that the monitoring of the glue-fixed mortar plumpness is needed.
The gray scale of the pixel points of the mortar area in the brick surface gray scale image is obviously different from the gray scale of the pixel points of the mortar area which is not smeared on the brick surface, and the actual mortar area in the brick surface image is generally extracted by using an area segmentation algorithm in the prior art. Because the mortar surface plumpness is uneven, part of edge mortar is raised, shadow is formed in the brick surface area, the shadow area has larger gray scale difference with the brick surface area, the shadow area is easily identified as a mortar area by mistake, and the accuracy of monitoring the plumpness of the glue-fixed mortar is reduced.
Disclosure of Invention
In order to solve the technical problem that a shadow area formed by a brick surface area is mistakenly identified as a mortar area by edge mortar bulge and reduce accuracy of monitoring the fullness of the glue-cured mortar, the invention aims to provide a glue-cured mortar fullness monitoring system based on computer vision, and the adopted technical scheme is as follows:
the invention provides a computer vision-based mortar plumpness monitoring system, which comprises:
the image preprocessing module is used for acquiring a brick surface gray image of the brick coated with the glue mortar;
the cluster point selection module is used for acquiring the gray level difference degree of each pixel point in the brick surface gray level image according to the difference between the gray level value of each pixel point and the gray level value of the pixel point in the preset window; screening a mortar clustering point and at least two brick surface clustering points from pixel points in the brick surface gray level image based on the gray level difference;
the mortar area acquisition module is used for taking the brick surface clustering point closest to each pixel point as a reference clustering point of each pixel point in the brick surface gray level image; combining the difference between the gray level difference degree of each pixel point and the reference clustering point and the gray level difference degree of the mortar clustering point of each pixel point in the position of each pixel point in the brick face gray level image to obtain the mortar possibility degree of each pixel point in the brick face gray level image; acquiring an actual mortar area in the brick surface gray level image according to the mortar possibility;
and the glue-cured mortar fullness monitoring module is used for monitoring the glue-cured mortar fullness based on the area of the actual mortar area.
Further, the method for obtaining the gray scale difference degree of each pixel point in the brick face gray scale image comprises the following steps:
selecting any pixel point in the brick surface gray level image as an analysis pixel point, and taking the difference between the analysis pixel point and the gray level value of each pixel point except the pixel point in a preset window as the initial difference degree of the analysis pixel point;
taking the average value of the initial difference degree of the analysis pixel points as the gray level difference degree of the analysis pixel points.
Further, the method for screening out one mortar cluster point and at least two brick surface cluster points from the pixel points in the brick surface gray level image based on the gray level difference degree comprises the following steps:
a preset mortar area and at least two preset brick surface areas exist in the brick surface gray level image;
taking a pixel point corresponding to the maximum gray level difference degree in a preset mortar area as a mortar clustering point;
and taking the pixel point corresponding to the minimum gray level difference degree in each preset brick surface area as a brick surface clustering point of each preset brick surface window.
Further, the method for acquiring the mortar probability of each pixel point in the brick face gray level image by combining the difference between the gray level difference degree of each pixel point and the reference clustering point and the difference between the gray level difference degree of the reference clustering point and the gray level difference degree of the mortar clustering point, wherein the position of each pixel point in the brick face gray level image is located, comprises the following steps:
acquiring a first difference adjustment parameter of each pixel point in the brick surface gray level image according to the position of each pixel point in the brick surface gray level image;
obtaining a second difference adjustment parameter of each pixel point in the brick surface gray image according to the difference between the gray difference degree of each pixel point in the brick surface gray image and the reference clustering point and the difference between the gray difference degree of the pixel point and the mortar clustering point;
and adjusting the difference between the gray values of the pixel points and the reference clustering points and the difference between the gray values of the pixel points and the mortar clustering points respectively based on the first difference adjustment parameter and the second difference adjustment parameter of each pixel point in the brick face gray image, so as to obtain the mortar possibility of each pixel point in the brick face gray image.
Further, the method for obtaining the first difference adjustment parameter of each pixel point in the brick surface gray level image according to the position of each pixel point in the brick surface gray level image comprises the following steps:
selecting any pixel point in the brick surface gray level image as an analysis pixel point, and taking a line segment between the analysis pixel point and a reference clustering point as a first line segment of the analysis pixel point; taking a line segment between the analysis pixel point and the mortar clustering point as a second line segment of the analysis pixel point;
respectively obtaining gradient values of each pixel point on a first line segment and a second line segment of the analysis pixel point; taking the maximum value of the gradient values of the pixel points on the first line segment as a gradient brick surface characteristic value of the analysis pixel points, and taking the maximum value of the gradient values of the pixel points on the second line segment as a gradient mortar characteristic value of the analysis pixel points;
and acquiring the first difference adjustment parameter of each pixel point in the brick surface gray level image according to the difference between the gradient brick surface characteristic value and the gradient mortar characteristic value of each pixel point in the brick surface gray level image.
Further, the calculation formula of the first difference adjustment parameter of each pixel point in the brick surface gray level image is as follows:
wherein G is a first difference adjustment parameter of each pixel point in the brick surface gray level image;the characteristic value of the gradient brick surface for each pixel point in the brick surface gray level image;the characteristic value of the gradient mortar for each pixel point in the brick surface gray level image; exp is an exponential function based on a natural constant e; sgn is a sign function; max is a maximum function.
Further, the calculation formula of the second difference adjustment parameter of each pixel point in the brick surface gray level image is as follows:
wherein F is the second difference adjustment parameter of each pixel point in the brick surface gray level image; v is the gray scale difference of each pixel point in the brick surface gray scale image;the gray level difference degree of the reference clustering point of each pixel point in the brick surface gray level image;the gray level difference degree of the mortar clustering points in the brick surface gray level image; exp is an exponential function based on a natural constant e; sgn is a sign function;as a function of absolute value.
Further, the calculation formula of the mortar probability of each pixel point in the brick surface gray level image is as follows:
wherein P is brick face ashMortar probability of each pixel point in the degree image;adjusting parameters for the first difference of each pixel point in the brick face gray level image; f is the second difference adjustment parameter of each pixel point in the brick surface gray level image; h is the gray value of each pixel point in the brick surface gray image;the gray value of a reference clustering point of each pixel point in the brick face gray image is obtained;the gray value of the mortar clustering point in the brick surface gray image;is a preset positive number;as a function of absolute value.
Further, the method for acquiring the actual mortar area in the brick surface gray level image according to the mortar probability degree comprises the following steps:
and taking a connected domain formed by pixel points with the mortar probability larger than a preset threshold value in the brick surface gray level image as an actual mortar region.
Further, the method for monitoring the fullness of the glue-cured mortar based on the area of the actual mortar area comprises the following steps:
and taking the ratio of the number of the pixel points in the actual mortar area to the number of the pixel points in the brick surface gray level image as the mortar plumpness.
The invention has the following beneficial effects:
in the embodiment of the invention, an area which is not coated possibly exists on the brick surface coated with the glue-fixed mortar, the gray distribution in the brick surface of the mortar and the brick surface which is not coated with the mortar is greatly different, the gray difference degree of the pixel points is obtained, and the mortar clustering points and the brick surface clustering points which respectively represent the characteristics of the two types of areas are screened for convenient analysis; the difference of the heights of the edge part of the mortar and the brick surface area causes a shadow area to be formed in the brick surface area, so that the gray value change degree of the edge part of the brick surface area and the mortar area is larger, the gray distribution of the brick surface area which is not coated with the glue mortar is more uniform, the gray distribution difference around the pixel point in the mortar area is larger, the gray difference degree reflects the gray value difference around the pixel point, compared with the gray difference degree of the pixel point and the reference clustering point, when the difference between the gray difference degree of the pixel point and the gray difference between the gray value of the pixel point and the gray value of the reference clustering point is smaller, the pixel point is more likely to be located in the mortar area, the two factors are combined for analysis, the possibility that the pixel point in the shadow area is mistakenly identified as the pixel point of the mortar can be reduced, and the obtained possibility that the pixel point is located in the actual mortar area can be accurately judged; the actual mortar area obtained based on the mortar possibility is more accurate, so that the accuracy of monitoring the fullness of the glue-cured mortar is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a computer vision-based mortar fullness monitoring system, according to an embodiment of the invention;
fig. 2 is a schematic view of a tile surface gray scale image according to one embodiment of the present invention.
Detailed Description
An embodiment of a computer vision-based mortar plumpness monitoring system comprises:
in order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the computer vision-based mortar fullness monitoring system according to the invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
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 invention belongs.
The invention aims at the specific scene: when calculating the plumpness of the glue mortar in a construction site using the glue mortar, the gray value of the pixel points of the partial background area in the detection surface is relatively close to the gray value of the pixel points of the target area, so that the extracted target area contains the pixel points of the partial background area.
The invention provides a concrete scheme of a mortar filling degree monitoring system based on computer vision, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a system block diagram of a system for monitoring the fullness of a mortar based on computer vision according to an embodiment of the invention is shown, the system includes: the device comprises an image preprocessing module 101, a cluster point selecting module 102, a mortar area obtaining module 103 and a glue mortar fullness monitoring module 104.
The image preprocessing module 101 is used for acquiring a brick surface gray image of a brick coated with the glue mortar.
Specifically, in a construction site of which the plumpness of the glue-fixed mortar is to be detected, a black cloth is used for covering a part of area, a brick coated with the glue-fixed mortar is placed in the center of the black cloth, the area of the black cloth needs to be larger than that of the brick, the face coated with the glue-fixed mortar in the brick is placed in front, and an industrial camera is used for photographing the face coated with the glue-fixed mortar, so that an original image of the face is obtained. Wherein, the original image of the brick surface is an RGB image.
And carrying out graying treatment on the original image of the brick surface to obtain the original gray image of the brick surface. In order to eliminate the influence of black cloth background on subsequent analysis, in the embodiment of the invention, the black cloth background area with the gray value of 0 in the original gray image of the brick surface is removed by threshold segmentation, and the brick area is left as the gray image of the brick surface. Fig. 2 is a schematic view of a tile surface gray scale image according to one embodiment of the present invention. In the embodiment of the invention, the threshold value of the threshold segmentation takes the empirical value of 5, and an implementer can set the threshold value according to specific situations.
In the embodiment of the present invention, a weighted average graying algorithm is selected to perform graying processing, and the specific method for dividing the image by threshold segmentation is not described herein, and is a technical means well known to those skilled in the art. In other embodiments of the present invention, the gray scale processing may be performed by an average value method, and the gray scale image of the brick surface is obtained by semantic segmentation, which are all technical means known to those skilled in the art, and are not limited herein.
The cluster point selection module 102 is configured to obtain a gray level difference degree of each pixel point in the brick surface gray level image according to a difference between the gray level value of each pixel point and a gray level value of the pixel point in a preset window; and screening out a mortar clustering point and at least two brick surface clustering points from pixel points in the brick surface gray level image based on the gray level difference.
Specifically, the glue-fixed mortar is formed by adding water to certain sand, cement, lime paste and the like, the gray values of pixel points in a mortar area and surrounding pixel points are greatly different, and the color of a brick surface area without the glue-fixed mortar is consistent, namely the gray distribution is relatively uniform. Therefore, the gray level difference degree of the pixel point is obtained according to the difference between the gray level values of the pixel point and the surrounding pixel points.
Preferably, the specific method for acquiring the gray scale difference of the pixel point comprises the following steps: selecting any pixel point in the brick surface gray level image as an analysis pixel point, and taking the difference between the analysis pixel point and the gray level value of each pixel point except the pixel point in a preset window as the initial difference degree of the analysis pixel point; taking the average value of the initial difference of the analysis pixel points as the gray level difference of the analysis pixel points.
It should be noted that, the number of the initial differences of the pixels is equal to the number of pixels except the pixels in the preset window of the pixels. In the embodiment of the invention, the size of the preset window takes an empirical valueThe implementer can set up by himself according to the specific situation; the number M of the pixels except the pixels in the preset window of the pixels is 8. Wherein, the pixel point is positioned at the center of the preset window.
According to the difference between the gray values of the pixel points and the pixel points in the preset window, the gray difference degree of each pixel point in the brick face gray image is obtained, and the gray difference degree is calculated according to the following formula:
wherein V is the gray scale difference of each pixel point in the brick surface gray scale image; h is the gray value of each pixel point in the brick surface gray image;the gray value of the mth pixel point except the pixel point in a preset window of each pixel point in the brick surface gray image is obtained; m is the number of pixels except the pixel in a preset window of each pixel in the brick surface gray level image.
When (when)The larger the difference between the gray values of the pixel points and the surrounding pixel points is, the larger the gray difference V is, and the greater the possibility that the pixel points are positioned in the mortar area is; when (when)The smaller the pixel point is, the more uniform the gray level distribution of the pixel point and the surrounding pixel points is, the smaller the gray level difference V is, and the greater the possibility that the pixel point is positioned in the brick surface area without mortar.
In engineering construction, it is required to spread the glue mortar on one brick, and the construction worker usually spreads the glue mortar on the middle position of the brick surface, but the four corner positions of the brick surface may not be spread with the glue mortar, so that the middle area of the gray image of the brick surface is more likely to be a mortar area, and the four corner areas are more likely to be brick surface areas not spread with the glue mortar, as shown in fig. 2.
In the process of clustering pixel points in the brick surface gray level image based on gray level values, five clustering centers exist in the brick surface gray level image due to the color difference between the mortar area and the brick surface area and the distribution position of the brick surface area and the mortar area, namely, one clustering center exists in each brick surface area, and one clustering center exists in the mortar area. Therefore, in the embodiment of the invention, a preset mortar area and four preset brick surface areas exist in the brick surface gray level image.
The specific positions of the preset mortar area and the preset mortar area are as follows: acquiring a central pixel point of a brick surface gray level image, setting a preset mortar area by taking the central pixel point as a center, and taking an empirical value of the size of the preset mortar areaThe method comprises the steps of carrying out a first treatment on the surface of the Taking a preset brick surface area of an upper right corner in a brick surface gray level image as an example for analysis, taking a vertex A1 of the upper right corner of the image as a starting point, taking lengths of two edges of the corner as X line segments respectively, wherein two end points of one line segment are respectively pixel points A1 and A2, end points of the other line segment are respectively pixel points A1 and A3, vertical lines of the edge where each pixel point is located are respectively arranged at positions A2 and A3, an intersection point of the two vertical lines is a pixel point A4, and a quadrilateral area surrounded by the pixel points A1, A2, A3 and A4 is taken as the preset brick surface area. Presetting the size of the brick surface areaTake the experience value. As shown in fig. 2, the area corresponding to the white frame at the upper right corner in fig. 2 is a preset brick surface area; the method of determining the preset tile surface area for the other three angular positions of the image is similar to the method of determining the preset tile surface area for the upper right angular position.
It should be noted that, since the area of the area on the brick surface to which the glue mortar is applied is generally larger than that of the single brick surface to which no mortar is applied, the size of the preset mortar area is larger than that of the preset brick surface.
Taking a pixel point corresponding to the maximum gray level difference degree in a preset mortar area as a mortar clustering point; and taking the pixel point corresponding to the minimum gray level difference degree in each preset brick surface area as a brick surface clustering point of each preset brick surface window. In the embodiment of the invention, the brick surface gray level image is provided with one mortar clustering point and four brick surface clustering points.
A mortar area obtaining module 103, configured to use a brick surface cluster point closest to each pixel point as a reference cluster point of each pixel point in the brick surface gray level image; combining the difference between the gray level difference degree of each pixel point and the reference clustering point and the gray level difference degree of the mortar clustering point of each pixel point in the position of each pixel point in the brick surface gray level image to obtain the mortar possibility degree of each pixel point in the brick surface gray level image; and acquiring an actual mortar area in the brick surface gray level image according to the mortar possibility.
The characteristics of the pixel points in the brick surface gray level image are similar to those of the brick cluster points closest to the pixel points, so that the reference pixel points of the pixel points in the brick surface gray level image are acquired for facilitating subsequent analysis. The specific method comprises the following steps:
and for each pixel point in the brick surface gray level image, acquiring Euclidean distance between the pixel point and each brick surface clustering point, and taking the brick surface clustering point corresponding to the minimum Euclidean distance as a reference clustering point of the pixel point. It should be noted that, the brick surface clustering points and the mortar clustering points have no reference pixel points.
The gradient characteristics of the pixel points in the brick surface area without the glue mortar are greatly different from those of the pixel points in the mortar area; the gray level distribution of the brick surface area without the glue mortar is uniform, the gray level distribution difference around the pixel points in the mortar area is larger, the difference between the gray level difference degree of the pixel points and the reference clustering points is obtained, the difference between the gray level difference degree of the pixel points and the gray level difference degree of the mortar clustering points is obtained, and the size relationship between the two differences shows the possibility that the pixel points are positioned in the actual mortar area; and the characteristics are combined for analysis, so that the mortar possibility degree reflecting the possibility of the pixel point to the actual mortar area is more accurate.
Preferably, the concrete acquisition method of the mortar possibility of the pixel points comprises the following steps: acquiring a first difference adjustment parameter of each pixel point in the brick surface gray level image according to the position of each pixel point in the brick surface gray level image; obtaining a second difference adjustment parameter of each pixel point in the brick surface gray image according to the difference between the gray difference of each pixel point in the brick surface gray image and the reference clustering point and the difference between the gray difference of the pixel point and the mortar clustering point; and adjusting the difference between the gray values of the pixel points and the reference clustering points and the difference between the gray values of the pixel points and the mortar clustering points based on the first difference adjustment parameter and the second difference adjustment parameter of each pixel point in the brick face gray image, so as to obtain the mortar possibility of each pixel point in the brick face gray image.
It should be noted that the invention only analyzes the mortar probability of the pixel points except the mortar clustering points and the brick surface clustering points in the brick surface gray level image.
(1) And acquiring a first difference adjustment parameter of each pixel point in the brick face gray level image.
The obvious height difference between partial pixel points in the mortar area and the pixel points in the surrounding brick area causes a shadow area to be formed in the brick area, so that the larger gray level difference exists between the pixel points in the brick area and the pixel points in the adjacent mortar area, namely, the larger gradient value exists between the brick area and the edge pixel points of the mortar area. And acquiring a first adjustment difference parameter based on gradient feature analysis of the position of the pixel point.
Preferably, the specific obtaining method of the first adjustment difference parameter is as follows: selecting any pixel point in the brick surface gray level image as an analysis pixel point, and taking a line segment between the analysis pixel point and a reference clustering point as a first line segment of the analysis pixel point; taking a line segment between the analysis pixel point and the mortar clustering point as a second line segment of the analysis pixel point; respectively obtaining gradient values of each pixel point on a first line segment and a second line segment of the analysis pixel point; taking the maximum value of the gradient values of the pixel points on the first line segment as the characteristic value of the gradient brick surface of the analysis pixel point, and taking the maximum value of the gradient values of the pixel points on the second line segment as the characteristic value of the gradient mortar of the analysis pixel point; and acquiring a first difference adjustment parameter of each pixel point in the brick surface gray level image according to the difference between the gradient brick surface characteristic value and the gradient mortar characteristic value of each pixel point in the brick surface gray level image.
The calculation formula of the first difference adjustment parameter of each pixel point in the brick surface gray level image is as follows:
wherein G is a first difference adjustment parameter of each pixel point in the brick surface gray level image;gradient brick surface characteristic values of each pixel point in the brick surface gray level image;the characteristic value of the gradient mortar for each pixel point in the brick surface gray level image; exp is an exponential function based on a natural constant e; sgn is a sign function; max is a maximum function.
When the pixel point is positioned near the mortar clustering point, the first line segment of the pixel point needs to pass through the edge formed by the mortar area and the brick surface area, and the second line segment of the pixel point is positioned in the mortar area, and the gradient value of the pixel point at the edge position is larger than the gradient value of the pixel point at the non-edge position, so that the characteristic value of the gradient brick surfaceCharacteristic value of gradient mortar，If the first difference adjustment parameter G is equal to 1 or 0, the first difference adjustment parameter G is greater than or equal to 1, which indicates that the greater the possibility that the pixel point is in the mortar area; when the pixel point is positioned near the brick surface cluster point, the first line segment of the pixel point is positioned inside the brick surface area, and the second line segment of the pixel point needs to pass through the mortar areaEdge formed with brick surface area, gradient brick surface characteristic valueCharacteristic value of gradient mortar，And if the first difference adjustment parameter G is equal to-1, the first difference adjustment parameter G is smaller than 1, and the pixel point is more likely to be in the brick surface area without the glue mortar.
(2) And obtaining a second difference adjustment parameter of each pixel point in the brick face gray level image.
Because the surface plumpness of the mortar is uneven, part of edge mortar is raised, shadow is formed in a brick surface area which is not coated with the mortar, namely pixel points in the brick surface area possibly contain under the shadow of the edge mortar, compared with the mortar area, the gray level difference of the pixel points in the brick surface area is smaller, and the second difference adjustment parameter is analyzed according to the characteristic that the pixel points in different areas have different gray level differences.
The calculation formula of the second difference adjustment parameter of each pixel point in the brick surface gray level image is as follows:
wherein F is a second difference adjustment parameter of each pixel point in the brick surface gray level image; v is the gray scale difference of each pixel point in the brick surface gray scale image;the gray level difference degree of the reference clustering point of each pixel point in the brick surface gray level image;the gray level difference degree of the mortar clustering points in the brick surface gray level image; exp is an exponential function based on a natural constant e; sgn is a sign function;as a function of absolute value.
As known, the gray values of the pixel points in the mortar area and the surrounding pixel points have larger difference, and the gray distribution in the brick surface area without the glue mortar is more uniform. When the gray scale difference V of the pixel point is smaller,the second difference adjustment parameter F is smaller than 1, and the possibility that the pixel point is in the brick surface area without the glue mortar is higher; when the gray scale difference V of the pixel point is larger,the greater the probability that the pixel point is a pixel point in the mortar region, the second difference adjustment parameter F is greater than or equal to 1.
(3) And (5) acquiring the mortar probability of each pixel point in the brick surface gray level image.
The most detailed difference between the mortar area and the brick surface area is that the gray values have larger difference, the first difference adjustment parameter analyzes the gradient characteristics of the positions of the pixel points, the second difference adjustment parameter analyzes the gray distribution around the pixel points, and the difference between the gray values of the pixel points is adjusted by combining two factors, so that the accuracy of recognizing the pixel points in the actual mortar area by the mortar probability is improved.
The calculation formula of the mortar probability of each pixel point in the brick surface gray level image is as follows:
wherein P is the mortar probability of each pixel point in the brick surface gray level image;adjusting parameters for the first difference of each pixel point in the brick surface gray level image; f is a second difference adjustment parameter of each pixel point in the brick surface gray level image; h is aThe gray value of each pixel point in the brick surface gray image is obtained;the gray value of a reference clustering point of each pixel point in the brick face gray image is obtained;the gray value of the mortar clustering point in the brick surface gray image;taking an empirical value of 0.01 for a preset positive number, and preventing the denominator from being zero to cause nonsensical denominator;as a function of absolute value.
The gray value difference between the pixel points of the brick area and the pixel points of the mortar area in the brick surface gray image is larger; when the gray value of the pixel point is closer to that of the pixel point of the mortar area, namelyThenThe larger the pixel point is, the greater the possibility that the pixel point is the pixel point of the mortar area is; when the gray value of the pixel point is closer to the gray value of the pixel point in the brick surface area, thenThe smaller the pixel point is, the greater the likelihood that the pixel point is the pixel point of the brick face area where no mortar is applied.
By judging the gradient characteristics around the position of the pixel point, the first difference is utilized to adjust the parameter G pairAdjusting according to the gray distribution characteristics around the pixel point, using the second difference adjusting parameter FAdjusting; gradient characteristics around the pixel point andthe closer the surrounding gray distribution is to the characteristics of the mortar clustering points, namely when G is greater than or equal to 1, and G is greater, F is greater than or equal to 1, and F is greater, the greater the possibility that the pixel points are the pixel points in the mortar area is, the greater the mortar probability P is; when the gradient characteristics around the pixel points and the surrounding gray level distribution are closer to the characteristics of the brick surface clustering points, namely when G is smaller than 1, F is smaller than 1, and the probability that the pixel points are the pixel points in the brick surface area is higher, the mortar probability P is lower.
The mortar clustering points and the brick surface clustering points are not analyzed for the mortar possibility, and each pixel point except the mortar clustering points and the brick surface clustering points in the brick surface gray level image has the corresponding mortar possibility P. The mortar cluster points are necessarily the pixel points of the mortar area, and the brick surface cluster points are not necessarily the pixel points of the mortar area.
And taking a connected domain formed by pixel points with the mortar probability larger than a preset threshold value in the brick surface gray level image as an actual mortar region. It should be noted that the mortar cluster points and the pixel points with the mortar probability greater than a preset threshold in the brick surface gray level image jointly form an actual mortar area.
It should be noted that, according to the analysis of the mortar possibility, the positive integer 1 is the boundary between the pixel points of the mortar area and the brick surface area, and the preset threshold value in the embodiment of the invention takes the empirical value 1, so that the implementer can set according to specific situations. And taking the pixel points with the mortar probability of more than 1 as the pixel points of the mortar area, and taking the pixel points with the mortar probability of less than 1 as the pixel points of the brick surface area without mortar.
And the glue-cured mortar fullness monitoring module 104 is used for monitoring the glue-cured mortar fullness based on the area of the actual mortar area.
Mortar fullness is the effective degree of adhesion of mortar in the mortar joints of the masonry of a brick to the brick, expressed as a percentage of the mortar area on the face of the brick. And measuring the area of the actual mortar area by the number of pixel points in the actual mortar area.
Preferably, the method for acquiring the mortar plumpness comprises the following steps: and taking the ratio of the number of the pixel points in the actual mortar area to the number of the pixel points in the brick surface gray level image as the mortar plumpness. The calculation formula of the mortar fullness of the brick surface is as follows:
wherein D is the mortar fullness of the brick surface coated with the glue-fixed mortar; r is the number of pixel points in the actual mortar area; r is the number of pixel points in the brick surface gray level image.
When r is larger, the larger the area of the actual mortar area on the brick surface is, the larger the mortar fullness D is, namely, the closer D is to 1, and the better the effective bonding effect of the glue-fixed mortar on the brick is; when r is smaller, the smaller the area of the actual mortar area on the brick surface is, the smaller the mortar fullness D is, namely, the closer D is to 0, the poorer the effective bonding effect of the glue-fixed mortar on the brick is.
The present invention has been completed.
To sum up, in the embodiment of the invention, the gray level difference degree of the pixel point in the brick surface gray level image is obtained, the mortar clustering point in the brick surface gray level image and the brick surface clustering point are set, and the mortar probability degree of the pixel point is obtained according to the difference between the gray level difference degree of the pixel point and the reference pixel point and the position of the pixel point in the brick surface gray level image and the difference between the gray level difference degree of the pixel point and the mortar clustering point; and acquiring an actual mortar area based on the mortar possibility, and monitoring the fullness of the glued mortar according to the area of the actual mortar area. According to the invention, whether the pixel points are positioned in the mortar area is judged by the mortar possibility of the pixel points, so that the actual mortar area is more accurate, and the accuracy of monitoring the fullness of the glue-cured mortar is improved.
An embodiment of a glue-bonded mortar area division system:
in the prior art, an actual mortar area is obtained from a brick surface gray level image through threshold segmentation, a shadow area is formed in the brick surface area which is not smeared with mortar by the mortar edge, and the shadow area is close to the shadow of the mortar area, so that the shadow area is easily identified as the mortar area, and the area of the actual mortar area has errors.
In order to solve the technical problem that the shadow generated by the edge mortar in the brick surface area is mistakenly recognized as mortar, so that the actual mortar area has errors, the invention aims to provide a glue-fixed mortar area segmentation system, which adopts the following specific technical scheme:
the image preprocessing module 101 is used for acquiring a brick surface gray image of a brick coated with the glue mortar;
the cluster point selection module 102 is configured to obtain a gray level difference degree of each pixel point in the brick surface gray level image according to a difference between the gray level value of each pixel point and a gray level value of the pixel point in a preset window; screening a mortar clustering point and at least two brick surface clustering points from pixel points in the brick surface gray level image based on the gray level difference;
a mortar area obtaining module 103, configured to use a brick surface cluster point closest to each pixel point as a reference cluster point of each pixel point in the brick surface gray level image; combining the difference between the gray level difference degree of each pixel point and the reference clustering point and the gray level difference degree of the mortar clustering point of each pixel point in the position of each pixel point in the brick face gray level image to obtain the mortar possibility degree of each pixel point in the brick face gray level image; and acquiring an actual mortar area in the brick surface gray level image according to the mortar probability.
The embodiment of the invention provides a glue-fixed mortar region segmentation system, which has the following technical effects:
in the embodiment of the invention, an area which is not coated possibly exists on the brick surface coated with the glue-fixed mortar, the gray distribution in the brick surface of the mortar and the brick surface which is not coated with the mortar is greatly different, the gray difference degree of the pixel points is obtained, and the mortar clustering points and the brick surface clustering points which respectively represent the characteristics of the two types of areas are screened for convenient analysis; the difference of the heights of the edge part of the mortar and the brick surface area causes a shadow area to be formed in the brick surface area, so that the gray value change degree of the edge part of the brick surface area and the mortar area is larger, the gray distribution of the brick surface area which is not coated with the glue mortar is more uniform, the gray distribution difference around the pixel point in the mortar area is larger, the gray difference degree reflects the gray value difference around the pixel point, compared with the gray difference degree of the pixel point and the reference clustering point, when the difference between the gray difference degree of the pixel point and the gray difference between the gray value of the pixel point and the gray value of the reference clustering point is smaller, the pixel point is more likely to be located in the mortar area, the two factors are combined for analysis, the possibility that the pixel point in the shadow area is mistakenly identified as the pixel point of the mortar can be reduced, and the obtained possibility that the pixel point is located in the actual mortar area can be accurately judged; the actual mortar area obtained based on the mortar probability is more accurate.
The image preprocessing module 101, the cluster point selecting module 102, and the mortar area obtaining module 103 are described in detail in the embodiment of the above-mentioned system for monitoring the fullness of the mortar based on computer vision, and are not described in detail.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. The utility model provides a glued mortar plumpness monitored control system based on computer vision which characterized in that, this system includes:
the image preprocessing module is used for acquiring a brick surface gray image of the brick coated with the glue mortar;
the cluster point selection module is used for acquiring the gray level difference degree of each pixel point in the brick surface gray level image according to the difference between the gray level value of each pixel point and the gray level value of the pixel point in the preset window; screening a mortar clustering point and at least two brick surface clustering points from pixel points in the brick surface gray level image based on the gray level difference;
the mortar area acquisition module is used for taking the brick surface clustering point closest to each pixel point as a reference clustering point of each pixel point in the brick surface gray level image; combining the difference between the gray level difference degree of each pixel point and the reference clustering point and the gray level difference degree of the mortar clustering point of each pixel point in the position of each pixel point in the brick face gray level image to obtain the mortar possibility degree of each pixel point in the brick face gray level image; acquiring an actual mortar area in the brick surface gray level image according to the mortar possibility;
and the glue-cured mortar fullness monitoring module is used for monitoring the glue-cured mortar fullness based on the area of the actual mortar area.
2. The system for monitoring the fullness of the mortar based on computer vision according to claim 1, wherein the method for obtaining the gray level difference of each pixel point in the gray level image of the brick surface comprises the following steps:
selecting any pixel point in the brick surface gray level image as an analysis pixel point, and taking the difference between the analysis pixel point and the gray level value of each pixel point except the pixel point in a preset window as the initial difference degree of the analysis pixel point;
taking the average value of the initial difference degree of the analysis pixel points as the gray level difference degree of the analysis pixel points.
3. The system for monitoring the fullness of the mortar based on computer vision according to claim 1, wherein the method for screening out a mortar cluster point and at least two brick surface cluster points from the pixel points in the brick surface gray level image based on the gray level difference comprises the following steps:
a preset mortar area and at least two preset brick surface areas exist in the brick surface gray level image;
taking a pixel point corresponding to the maximum gray level difference degree in a preset mortar area as a mortar clustering point;
and taking the pixel point corresponding to the minimum gray level difference degree in each preset brick surface area as a brick surface clustering point of each preset brick surface window.
4. The system for monitoring the fullness of the mortar based on computer vision according to claim 1, wherein the method for obtaining the mortar probability of each pixel point in the gray image of the brick surface comprises the steps of:
acquiring a first difference adjustment parameter of each pixel point in the brick surface gray level image according to the position of each pixel point in the brick surface gray level image;
obtaining a second difference adjustment parameter of each pixel point in the brick surface gray image according to the difference between the gray difference degree of each pixel point in the brick surface gray image and the reference clustering point and the difference between the gray difference degree of the pixel point and the mortar clustering point;
and adjusting the difference between the gray values of the pixel points and the reference clustering points and the difference between the gray values of the pixel points and the mortar clustering points respectively based on the first difference adjustment parameter and the second difference adjustment parameter of each pixel point in the brick face gray image, so as to obtain the mortar possibility of each pixel point in the brick face gray image.
5. The system for monitoring the fullness of the mortar based on computer vision according to claim 4, wherein the method for obtaining the first difference adjustment parameter of each pixel point in the brick surface gray level image according to the position of each pixel point in the brick surface gray level image comprises the following steps:
selecting any pixel point in the brick surface gray level image as an analysis pixel point, and taking a line segment between the analysis pixel point and a reference clustering point as a first line segment of the analysis pixel point; taking a line segment between the analysis pixel point and the mortar clustering point as a second line segment of the analysis pixel point;
respectively obtaining gradient values of each pixel point on a first line segment and a second line segment of the analysis pixel point; taking the maximum value of the gradient values of the pixel points on the first line segment as a gradient brick surface characteristic value of the analysis pixel points, and taking the maximum value of the gradient values of the pixel points on the second line segment as a gradient mortar characteristic value of the analysis pixel points;
and acquiring the first difference adjustment parameter of each pixel point in the brick surface gray level image according to the difference between the gradient brick surface characteristic value and the gradient mortar characteristic value of each pixel point in the brick surface gray level image.
6. The computer vision-based mortar turgor monitoring system as defined in claim 5, wherein the calculation formula of the first difference adjustment parameter of each pixel point in the brick surface gray level image is as follows:
wherein G is a first difference adjustment parameter of each pixel point in the brick surface gray level image;the characteristic value of the gradient brick surface for each pixel point in the brick surface gray level image; />The characteristic value of the gradient mortar for each pixel point in the brick surface gray level image; exp is an exponential function based on a natural constant e; sgn is a sign function; max is a maximum function.
7. The computer vision-based mortar turgor monitoring system as defined in claim 4, wherein the calculation formula of the second difference adjustment parameter of each pixel point in the brick surface gray level image is as follows:
wherein F is the second difference adjustment parameter of each pixel point in the brick surface gray level image; v is the gray scale difference of each pixel point in the brick surface gray scale image;the gray level difference degree of the reference clustering point of each pixel point in the brick surface gray level image;the gray level difference degree of the mortar clustering points in the brick surface gray level image; exp is an exponential function based on a natural constant e; sgn is a sign function; />As a function of absolute value.
8. The system for monitoring the fullness of the mortar based on computer vision according to claim 4, wherein the calculation formula of the mortar probability of each pixel point in the brick surface gray level image is as follows:
wherein P is the mortar probability of each pixel point in the brick surface gray level image;adjusting parameters for the first difference of each pixel point in the brick face gray level image; f is the second difference adjustment parameter of each pixel point in the brick surface gray level image; h is the gray value of each pixel point in the brick surface gray image; />For each image in the brick surface gray level imageGray values of reference cluster points of the pixel points; />The gray value of the mortar clustering point in the brick surface gray image; />Is a preset positive number; />As a function of absolute value.
9. The system for monitoring the fullness of the mortar on the basis of computer vision according to claim 1, wherein the method for acquiring the actual mortar area in the gray image of the brick surface according to the mortar probability comprises the following steps:
and taking a connected domain formed by pixel points with the mortar probability larger than a preset threshold value in the brick surface gray level image as an actual mortar region.
10. The system for monitoring the fullness of the mortar based on computer vision according to claim 1, wherein the method for monitoring the fullness of the mortar based on the area of the actual mortar area comprises the following steps:
and taking the ratio of the number of the pixel points in the actual mortar area to the number of the pixel points in the brick surface gray level image as the mortar plumpness.
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