CN115908411B - Concrete curing quality analysis method based on visual detection - Google Patents
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Abstract
The invention discloses a concrete curing quality analysis method based on visual detection, which relates to the field of image processing, and comprises the following steps: acquiring an edge line in a gray level image of the surface of the concrete to be detected; obtaining the possibility of cracking of each edge line according to the gray average value and the curvature of each edge line; obtaining a first edge line and a second edge line by using the possibility of cracking; acquiring a target edge line and a target endpoint of each first edge line; recalculating the possibility of cracking of the extended first edge line; and determining crack edge lines according to the crack possibility of the first edge lines and the extended first edge lines and the number of the second edge lines in the minimum circumscribed rectangle, and evaluating the curing quality of the concrete according to the area occupation ratio of the crack edge lines in the gray level image. The invention improves the accuracy of crack defect detection, and further ensures that the obtained concrete curing quality evaluation result is more accurate.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a concrete curing quality analysis method based on visual detection.
Background
The concrete curing method is a sub-project with the longest time consumption and the greatest influence on the concrete quality in the whole concrete project. The concrete curing generally adopts a method of natural curing by sprinkling water, curing by spraying a film and curing by wrapping a plastic film to keep the concrete moist, and the purpose of curing is achieved by avoiding water loss. However, many projects are careless to maintain, so that the hydration effect of the final concrete is poor, cracks appear, and the service life of the projects can be seriously influenced. Therefore, the maintenance quality inspection is required to be continuously carried out in the concrete maintenance process and immediately after the completion of the maintenance, and whether cracks appear is mainly detected.
In the prior art, edge detection is usually used for obtaining crack defects, but the concrete surface also has groove marks similar to the form of the crack defects, the crack is generated due to the damaged quality and performance of the concrete, and the groove marks are surface changes caused by misoperation and have little influence on the quality; therefore, only a crack region with defects needs to be obtained, but the groove mark and the crack defect are in an elongated curve form in a gray level image, so that when the edge detection is performed, the edge lines of the groove mark and the crack defect on the surface of the concrete are similar, are difficult to distinguish, and cannot obtain accurate crack defects, so that the maintenance quality of the concrete cannot be accurately analyzed.
Disclosure of Invention
The invention provides a concrete curing quality analysis method based on visual detection, which aims to solve the problem that the groove mark and crack defect on the concrete surface are difficult to distinguish in the existing edge detection process.
The invention relates to a concrete curing quality analysis method based on visual detection, which adopts the following technical scheme:
acquiring a gray image of the surface of the concrete to be detected;
acquiring an edge line in a gray level image and a non-edge pixel point in the middle of a double threshold value during edge detection, wherein the non-edge pixel point is used as a candidate pixel point;
obtaining the curvature of each edge line according to the number of the pixel points on each edge line and the coordinates of the pixel points; obtaining the possibility of cracking of each edge line according to the gray average value and the curvature of each edge line;
dividing the edge line into a first edge line and a second edge line according to the crack probability, wherein the crack probability of the first edge line is greater than that of the second edge line;
obtaining a target edge line with the nearest distance between each first edge line and other first edge line endpoints by utilizing the distance between each first edge line endpoint and the other first edge line endpoints, and taking the endpoint with the nearest distance between each first edge line and the target edge line as a target endpoint;
determining whether the first edge lines are extended or not according to whether candidate pixel points exist in the neighborhood of the target end point of each first edge line and the distance between the candidate pixel points and the end points of the corresponding target edge lines when the candidate pixel points exist, and recalculating the possibility of cracks on the extended first edge lines;
and marking the first edge line which is not extended and the first edge line which is extended as third edge lines, determining crack edge lines according to the number of the second edge lines in the minimum circumscribed rectangle of each third edge line and the crack possibility of the third edge lines, and evaluating the curing quality of the concrete according to the area occupation ratio of the crack edge lines in the gray level image.
Further, the step of obtaining the curvature of each edge line includes:
obtaining the absolute value of the slope of the connecting line of the starting point and the end point of each edge line according to the coordinates of the starting point and the end point of the pixel point on each edge line in the gray level image, and taking the absolute value as a reference slope;
obtaining the absolute value of the slope of each pixel point and the connecting line of the starting point according to the coordinates of each pixel point and the starting point on each edge line;
and solving an average value of the absolute value of the difference between the absolute value of the slope of each pixel point on each edge line and the starting point connecting line and the reference slope, and normalizing the obtained average value to obtain the curvature of each edge line.
Further, the step of determining whether to extend the first edge line comprises:
taking a target endpoint of the first edge line as a seed point, and if no candidate pixel point exists in the neighborhood of the seed point, not extending the first edge line;
if candidate pixel points exist in the neighborhood of the seed point, acquiring the candidate pixel point with the largest gradient in the neighborhood as a target candidate point;
if the minimum distance between the target candidate point and the corresponding target edge line end point is not smaller than the distance between the target end point and the target edge line end point, the first edge line is not extended, if so, the first edge line is extended to the target candidate pixel point, and the target candidate pixel point is continuously used as a new seed point to determine whether the first edge line is extended.
Further, the formula for obtaining the crack probability of each edge line is:
wherein ,
represent the first
The possibility of cracking of the edge lines;
represent the first
Bending of the strip edge line;
represent the first
Bending of the strip edge line;
representing the total number of edge lines in the gray scale image;
the curvature average value of all edge lines in the gray level image is represented;
represent the first
A normalized value of the gray average value of the edge lines;
represent the first
A normalized value of the gray average value of the edge lines;
a mean value of normalized values representing the gray mean value of all edge lines in the gray image;
is a normalization function.
Further, the step of determining the crack edge line includes:
the formula for obtaining the final fracture possibility of each third edge line is as follows:
wherein ,
represent the first
Final fracture possibility of the third edge line;
represent the first
A possibility of cracking of the third edge line;
represent the first
The area of the smallest circumscribed rectangle of the third edge line;
represent the first
The number of second edge lines in the minimum bounding rectangle of the third edge lines;
is an exponential function based on a natural constant e;
and determining a third edge line with the final fracture possibility being larger than a preset fracture threshold value as a fracture edge line.
Further, the step of obtaining the target edge line closest to each first edge line includes:
acquiring the minimum distance between the end point of each first edge line and the end point of each other first edge line;
selecting a minimum value from all minimum distances obtained from each first edge line;
and taking the first edge line corresponding to the minimum value as a target edge line with the first edge line nearest to the first edge line.
Further, the step of evaluating the curing quality of the concrete according to the area ratio of the crack edge line in the gray image includes:
if the area occupation ratio of the crack edge line in the gray level image is smaller than a preset first area threshold value, the curing quality of the concrete is qualified;
if the area occupation ratio of the crack edge line in the gray level image is larger than or equal to the first area threshold value and smaller than the preset second area threshold value, the curing quality of the concrete is poor;
if the area ratio of the crack edge line in the gray level image is larger than or equal to the second area threshold value, the curing quality of the concrete is not qualified.
Further, the step of dividing the edge line into a first edge line and a second edge line according to the likelihood of the crack includes:
setting a crack probability threshold, and taking an edge line with the crack probability larger than the crack probability threshold as a first edge line;
and taking an edge line with the crack probability less than or equal to the crack probability threshold as a second edge line.
The beneficial effects of the invention are as follows: according to the concrete curing quality analysis method based on visual detection, the crack probability of each edge line is obtained through the curvature and the gray level average value of each edge line, and because the edge line of the crack is more tortuous relative to the edge line of the groove mark, the curvature is larger, the direction of the edge line of the groove mark has consistency, and the brightness of the crack and the groove mark in a gray level image is different, namely the gray level value is different, the crack probability can be obtained by combining the curvature and the gray level average value of the edge line to carry out preliminary distinction, and a first edge line which is closer to the crack defect and a second edge line which is closer to the groove mark are obtained; because some tiny crack edges cannot be obtained by conventional edge detection, the first edge line is extended through the distance between the non-edge pixel points in the double-threshold interval and the nearest target edge line during edge detection, so that a complete extended first edge line is obtained; and as the number of the groove marks around the crack defect of the concrete surface is smaller than that of the groove marks around the groove marks, the crack edge line is further determined by combining the feature and the crack possibility of the first edge line, so that the obtained crack edge line is more accurate, and the quality evaluation result of the obtained concrete surface is more accurate.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the general steps of an embodiment of a visual inspection-based concrete curing quality analysis method of the present invention;
fig. 2 is a gray scale image of a concrete surface.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
An embodiment of a method for analyzing concrete curing quality based on visual inspection of the present invention is shown in fig. 1, and the method comprises:
s1, acquiring a gray image of the surface of the concrete to be detected; and acquiring an edge line in the gray level image and a non-edge pixel point which is positioned in the middle of the double threshold values during edge detection, wherein the non-edge pixel point is used as a candidate pixel point.
Specifically, an unmanned aerial vehicle provided with a professional camera is utilized to fly and collect an image of the concrete surface, the image is affected by mechanical noise in the image collecting process, noise points exist in the collected image, and Gaussian filtering is carried out on the collected image to obtain a noise-reduced image.
Graying treatment is carried out on the image after noise reduction to obtain a gray image of the concrete surface, as shown in fig. 2.
And carrying out edge detection on the gray level image by using a Canny operator to obtain edge lines in the gray level image, wherein the obtained edge lines comprise edge lines of cracks and groove marks of the concrete surface.
When the Canny operator performs edge detection, setting a double threshold, namely an upper threshold and a lower threshold, and if the pixel point gradient is larger than the upper threshold, the pixel point gradient is considered to be a boundary necessarily, namely a strong edge pixel point; if the pixel is smaller than the lower threshold value, the pixel is considered to be a non-edge pixel point; a pixel that is between the upper and lower threshold values is determined to be a weak edge pixel if it belongs to the octal of the strong edge pixel, and is determined to be a non-edge pixel if it does not belong to the octal of the strong edge pixel. However, in this scheme, since the crack region has permeability, these pixels that do not belong to the eight neighborhoods of edge pixels need to be further analyzed, and non-edge pixels that lie between the upper and lower threshold values are acquired as candidate pixels, and then analyzed.
S2, obtaining the curvature of each edge line according to the number of the pixel points on each edge line and the coordinates of the pixel points.
The crack is formed by the fact that concrete is damaged from inside to outside, the groove mark is a scratch on the surface of the concrete, the edge line of the crack defect is more tortuous and has higher curvature, and the edge line of the groove mark has stronger direction consistency, so that the crack and the groove mark can be distinguished preliminarily according to the curvature of the edge line.
Specifically, the number of pixel points and the coordinates of the pixel points on each edge line in the gray image are obtained.
Obtaining the absolute value of the slope of the connecting line of the starting point and the end point of each edge line according to the coordinates of the starting point and the end point of the pixel point on each edge line, and taking the absolute value as a reference slope; obtaining the absolute value of the slope of each pixel point and the connecting line of the starting point according to the coordinates of each pixel point and the starting point on each edge line; and solving an average value of the absolute value of the difference between the absolute value of the slope of each pixel point on each edge line and the starting point connecting line and the reference slope, and normalizing the obtained average value to obtain the curvature of each edge line. The formula for specifically calculating the curvature of each edge line is:
wherein ,
represent the first
Bending of the strip edge line;
represent the first
The number of pixel points on the strip edge line;
is the first
The coordinates of the start point of the edge line,
is the first
The coordinates of the end point of the line of edges,
is the first
On the first edge line
Coordinates of the pixel points, and a starting point and an ending point of the edge line are any one end point of the edge line;
is a normalization function;
represent the first
The absolute value of the slope of the connecting line between the starting point and the end point of the edge line is taken as a reference slope; from the second pixel point on the edge line
Starting traversal (the first pixel point is taken as the starting point), and respectively calculating the first pixel point
Individual pixels
And a starting point
And obtaining the curvature of the edge line by averaging the absolute values of all the differences and normalizing the absolute values of the slope of the line and the difference of the reference slope. Absolute value of slope obtained by each pixel point andthe reference slope is compared, because the starting point and the end point on the edge line are connected to determine a reference direction, if the edge line is a straight line, the direction of any pixel point on the edge line connected with the starting point is not greatly different from the reference direction, and if the difference is larger, the edge line is considered to deviate from the straight line, namely, the bending degree is larger. The normalization of the difference results is to facilitate the calculation of the likelihood of cracking for the gray value of the subsequent bonded edge line.
S3, obtaining the crack possibility of each edge line according to the gray average value and the curvature of each edge line; the edge lines are divided into a first edge line and a second edge line according to the likelihood of the crack, and the likelihood of the crack of the first edge line is greater than that of the second edge line.
The bending degree of the edge line of the crack is large, but since the edges of partial sections of the crack are not obvious, one continuous crack edge line can be divided into a plurality of sections of edge lines during edge detection, and the bending degree of each partial section of edge line is not high, so that the edge line of the crack can be detected as an edge line of a groove mark. Therefore, it is also necessary to distinguish between the crack and the groove mark from the gray scale, the gray scale of the groove mark in the gray scale image is brighter than the gray scale of the crack, if the gray scale of the pixel in the gray scale image is divided into three levels of low, medium and high, the crack is mainly represented by the low and medium levels, and the groove mark is mainly represented by the medium and high levels, so that the possibility that the edge line is the edge line of the crack is further determined according to the gray scale value and the bending degree.
Specifically, the gray value of each pixel point on each edge line is obtained, and the gray regularity of each edge line is calculated according to the following formula:
wherein ,
represent the first
Gray scale regularity of the strip edge line;
represent the first
The number of pixel points on the strip edge line;
represent the first
On the first edge line
Gray values of the individual pixels;
represent the first
Gray average value of pixel points on the edge lines;
is a normalization function; the gray average value of the pixel points on each edge line is used for representing the gray rule of the edge line, the larger the gray average value is, the brighter the gray in the gray image is, the more likely the gray average value is a groove mark edge line, the smaller the gray average value is, the more likely the gray average value is a crack edge line, and the normalization of the gray average value is used for conveniently combining the bending degree of the edge line to calculate the possibility of cracks in the follow-up process.
The formula for calculating the likelihood of cracking for each edge line is:
wherein ,
represent the first
The possibility of cracking of the edge lines;
represent the first
Bending of the strip edge line;
represent the first
Bending of the strip edge line;
representing the total number of edge lines in the gray scale image;
the curvature average value of all edge lines in the gray level image is represented;
represent the first
Normalized value of gray-scale mean of the edge lines, i.e. the first
Gray scale regularity of the strip edge line;
represent the first
Normalized value of gray-scale mean of the edge lines, i.e. the first
Gray scale regularity of the strip edge line;
the average value of the normalized values of the gray average values of all edge lines in the gray image is represented, namely the gray regular average value of all edge lines;
is a normalization function.
For the correlation coefficient of the curvature and the gray scale regularity, the curvature and the gray scale regularity are considered to be in negative correlation, so that the obtained correlation coefficient is a negative number, and for the convenience of representing the correlation degree, the absolute value of the correlation coefficient is taken, and the larger the absolute value is, the larger the negative correlation degree of the curvature and the gray scale regularity is; the ratio of the two is weighted by the negative correlation degree of the two, the larger the curvature is, the smaller the gray value is, and the larger the negative correlation degree of the two is, the greater the possibility that the edge line is a crack edge line is considered. And the obtained result is normalized, so that a threshold value is conveniently set to distinguish edge lines.
Setting a crack probability threshold, taking an edge line with the crack probability larger than the crack probability threshold as a first edge line, and taking an edge line with the crack probability smaller than or equal to the crack probability threshold as a second edge line, wherein the crack probability threshold is set to be 0.45 in the embodiment of the invention. The first edge line is more likely to be a slit edge line and the second edge line is more likely to be a groove edge line.
S4, obtaining a target edge line with the nearest distance between each first edge line and other first edge line endpoints by utilizing the distance between each first edge line endpoint and the other first edge line endpoints, and taking the endpoint with the nearest distance between each first edge line and the target edge line as the target endpoint.
Because the crack has strong permeability, the Canny edge cannot detect some tiny cracks, only the detected edge line is analyzed, and the undetected tiny cracks are not considered, so that the obtained detection result is not accurate enough, and further analysis can be performed through pixel points around the first edge line which is closer to the crack edge line.
Specifically, because the crack has strong permeability, the possibility that a tiny crack exists between two nearest first edge lines is high, the minimum distance between the end point of each first edge line and the end point of each other first edge line is obtained, each edge line has two end points, when the distance between the two first edge lines is calculated, one end point is selected from the two edge lines to be matched for carrying out distance calculation, four calculation results are obtained, and the minimum value in the four calculation results is selected as the minimum distance between the two first edge lines; selecting a minimum value from all minimum distances obtained from each first edge line; and taking other first edge lines corresponding to the selected minimum value as the nearest target edge line of the first edge line.
And taking the end point of each first edge line closest to the end point of the target edge line as a target end point.
S5, determining whether the first edge lines are extended or not according to whether candidate pixel points exist in the neighborhood of the target end point of each first edge line and the distance between the candidate pixel points and the end points of the corresponding target edge lines when the candidate pixel points exist, and recalculating the possibility of cracks of the extended first edge lines.
When the Canny operator detects the edges, non-edge pixel points which are partially between the upper and lower boundary thresholds exist, and the non-edge pixel points which are not judged to be weak edges are taken as candidate pixel points to be further analyzed in consideration of the permeability of cracks.
Specifically, taking a target endpoint of the first edge lines as a seed point, searching candidate pixel points in a 5*5 neighborhood of the seed point (the neighborhood size can be adjusted according to specific implementation conditions), and if no candidate pixel points exist in the neighborhood of the seed point, indicating that no fine cracks exist between the two first edge lines and the first edge lines are not extended; if candidate pixel points exist in the neighborhood of the seed point, acquiring the candidate pixel point with the largest gradient in the neighborhood as a target candidate point; if the minimum distance between the target candidate point and the corresponding target edge line end point is not smaller than the distance between the target end point and the target edge line end point, which means that no fine crack exists between the two first edge lines, the first edge line is not extended, and if the minimum distance is smaller than the minimum distance, the first edge line is extended to the target candidate pixel point.
And continuously taking the target candidate pixel point as a new seed point to determine whether to extend the first edge line or not until the candidate pixel point does not exist in the neighborhood of the new seed point, or stopping extending until the minimum distance between the target candidate pixel point and the corresponding target edge line end point is not smaller than the distance between the target end point and the target edge line end point, and obtaining the complete first edge line after extension.
The likelihood of cracking is recalculated for all extended first edge lines according to steps S2-S3.
S6, marking the first edge line which is not extended and the first edge line which is extended as third edge lines, determining crack edge lines according to the number of the second edge lines in the minimum circumscribed rectangle of each third edge line and the crack possibility of the third edge lines, and evaluating the curing quality of the concrete according to the area occupation ratio of the crack edge lines in the gray level image.
Since the kerf will present a clustered distribution on the concrete surface, there will be a plurality of second edges around the perimeter that are closer to the kerf edge line, and the final likelihood of a crack around the crack that is closer to the second edge of the kerf edge line can thus be calculated for each first edge line and the extended first edge line based on this feature.
Specifically, the first edge line which is not extended and the first edge line which is extended are marked as third edge lines, the minimum circumscribed rectangle is made for each third edge line, the number of the second edge lines in the minimum circumscribed rectangle of each third edge line is obtained, and the final crack possibility of each third edge line is calculated according to the following formula:
wherein ,
represent the first
Final fracture possibility of the third edge line;
represent the first
A possibility of cracking of the third edge line;
represent the first
The area of the smallest circumscribed rectangle of the third edge line;
represent the first
The number of second edge lines in the minimum bounding rectangle of the third edge lines;
is an exponential function based on a natural constant e;
the greater the number of second edge lines per unit area, the more likely the edge lines are to be groove mark edge lines, i.e. representing the number of second edge lines per unit area in the minimum bounding rectangle
The greater the ratio of (2)
After the negative correlation mapping, the smaller the possibility of final cracking of the edge line is obtained, the more the final cracking is in denominator
The larger the negative correlation mapping, the greater the likelihood of a final crack resulting in the edge line.
The negative correlation map is used to facilitate the selection of a fracture threshold to determine a fracture edge line from the third edge lines.
In the embodiment of the invention, the crack threshold value is set to be 0.5, and the third edge line with the final crack probability larger than the crack threshold value is determined as the crack edge line, so that all the crack edge lines in the gray level image are obtained. Meanwhile, the crack edge line in the gray level image can be marked, and the gray level image marked with the crack edge line is displayed on a corresponding display, so that the abnormal condition of the concrete surface can be more intuitively checked.
The ratio of the number of the pixel points on the edge lines of all cracks to the total number of the pixel points in the gray level image is obtained, the larger the ratio is, the worse the quality of the concrete surface is, the worse the concrete curing quality is, and a specific curing quality class implementer can set a threshold value of the ratio according to actual conditions.
In this embodiment, if the area ratio of the crack edge line in the gray level image is smaller than the preset first area threshold value of 0.3, the curing quality of the concrete is qualified, and only local cracks need to be processed in a targeted manner; if the area ratio of the crack edge line in the gray level image is larger than or equal to the first area threshold value 0.3 and smaller than the preset second area threshold value 0.7, the curing quality of the concrete is poor, and the area where the crack is needed to be repaired is located; if the area ratio of the crack edge line in the gray level image is greater than or equal to the second area threshold value of 0.7, the curing quality of the concrete is unqualified, the crack penetrates through the whole concrete surface, and the curing quality is poor.
In summary, the method for analyzing the concrete curing quality based on visual detection is provided, and the possibility of the crack of each edge line is obtained through the curvature and the gray level average value of each edge line, because the edge line of the crack is more tortuous relative to the edge line of the trench, the curvature is larger, the direction of the edge line of the trench has consistency, and the brightness of the crack and the trench in a gray level image is different, namely, the gray level value is different, the possibility of the crack can be obtained by combining the curvature and the gray level average value of the edge line to be primarily distinguished, and a first edge line which is closer to the crack defect and a second edge line which is closer to the trench are obtained; because some tiny crack edges cannot be obtained by conventional edge detection, the first edge line is extended through the distance between the non-edge pixel points in the double-threshold interval and the nearest target edge line during edge detection, so that a complete extended first edge line is obtained; and as the number of the groove marks around the crack defect of the concrete surface is smaller than that of the groove marks around the groove marks, the crack edge line is further determined by combining the feature and the crack possibility of the first edge line, so that the obtained crack edge line is more accurate, and the quality evaluation result of the obtained concrete surface is more accurate.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (4)
1. The concrete curing quality analysis method based on visual detection is characterized by comprising the following steps of:
acquiring a gray image of the surface of the concrete to be detected;
acquiring an edge line in a gray level image and a non-edge pixel point in the middle of a double threshold value during edge detection, wherein the non-edge pixel point is used as a candidate pixel point;
obtaining the curvature of each edge line according to the number of the pixel points on each edge line and the coordinates of the pixel points; obtaining the possibility of cracking of each edge line according to the gray average value and the curvature of each edge line;
the step of obtaining the curvature of each edge line comprises:
obtaining the absolute value of the slope of the connecting line of the starting point and the end point of each edge line according to the coordinates of the starting point and the end point of the pixel point on each edge line in the gray level image, and taking the absolute value as a reference slope;
obtaining the absolute value of the slope of each pixel point and the connecting line of the starting point according to the coordinates of each pixel point and the starting point on each edge line;
calculating an average value of the absolute value of the difference between the absolute value of the slope of each pixel point on each edge line and the starting point line and the absolute value of the reference slope, and normalizing the obtained average value to obtain the curvature of each edge line;
the formula for obtaining the crack probability of each edge line is:
wherein ,
indicate->
The possibility of cracking of the edge lines; />
Indicate->
Bending of the strip edge line; />
Indicate->
Bending of the strip edge line; />
Representing the total number of edge lines in the gray scale image; />
The curvature average value of all edge lines in the gray level image is represented; />
Indicate->
A normalized value of the gray average value of the edge lines; />
Indicate->
A normalized value of the gray average value of the edge lines; />
A mean value of normalized values representing the gray mean value of all edge lines in the gray image; />
Is a normalization function;
dividing the edge line into a first edge line and a second edge line according to the crack probability, wherein the crack probability of the first edge line is greater than that of the second edge line;
obtaining a target edge line with the nearest distance between each first edge line and other first edge line endpoints by utilizing the distance between each first edge line endpoint and the other first edge line endpoints, and taking the endpoint with the nearest distance between each first edge line and the target edge line as a target endpoint;
determining whether the first edge lines are extended or not according to whether candidate pixel points exist in the neighborhood of the target end point of each first edge line and the distance between the candidate pixel points and the end points of the corresponding target edge lines when the candidate pixel points exist, and recalculating the possibility of cracks on the extended first edge lines;
the step of determining whether to extend the first edge line comprises:
taking a target endpoint of the first edge line as a seed point, and if no candidate pixel point exists in the neighborhood of the seed point, not extending the first edge line;
if candidate pixel points exist in the neighborhood of the seed point, acquiring the candidate pixel point with the largest gradient in the neighborhood as a target candidate point;
if the minimum distance between the target candidate point and the corresponding target edge line end point is not smaller than the distance between the target end point and the target edge line end point, the first edge line is not extended, if so, the first edge line is extended to the target candidate pixel point, and the target candidate pixel point is continuously used as a new seed point to determine whether the first edge line is extended or not;
the first edge line which is not extended and the first edge line which is extended are marked as third edge lines, the crack edge lines are determined according to the number of the second edge lines in the minimum circumscribed rectangle of each third edge line and the crack possibility of the third edge lines, and the curing quality of the concrete is evaluated according to the area occupation ratio of the crack edge lines in the gray level image;
the step of evaluating the curing quality of the concrete according to the area ratio of the crack edge line in the gray level image comprises the following steps:
if the area occupation ratio of the crack edge line in the gray level image is smaller than a preset first area threshold value, the curing quality of the concrete is qualified;
if the area occupation ratio of the crack edge line in the gray level image is larger than or equal to the first area threshold value and smaller than the preset second area threshold value, the curing quality of the concrete is poor;
if the area ratio of the crack edge line in the gray level image is larger than or equal to the second area threshold value, the curing quality of the concrete is not qualified.
2. The visual inspection-based concrete curing quality analysis method according to claim 1, wherein the step of determining the crack edge line comprises:
the formula for obtaining the final fracture possibility of each third edge line is as follows:
wherein ,
indicate->
Final fracture possibility of the third edge line; />
Indicate->
A possibility of cracking of the third edge line; />
Indicate->
The area of the smallest circumscribed rectangle of the third edge line; />
Indicate->
The number of second edge lines in the minimum bounding rectangle of the third edge lines; />
Is an exponential function based on a natural constant e;
and determining a third edge line with the final fracture possibility being larger than a preset fracture threshold value as a fracture edge line.
3. The method for analyzing the quality of concrete curing based on visual inspection according to claim 1, wherein the step of obtaining the target edge line closest to each first edge line comprises:
acquiring the minimum distance between the end point of each first edge line and the end point of each other first edge line;
selecting a minimum value from all minimum distances obtained from each first edge line;
and taking the first edge line corresponding to the minimum value as a target edge line with the first edge line nearest to the first edge line.
4. The method for analyzing the quality of concrete curing based on visual inspection according to claim 1, wherein the step of dividing the edge line into a first edge line and a second edge line according to the possibility of cracks comprises:
setting a crack probability threshold, and taking an edge line with the crack probability larger than the crack probability threshold as a first edge line;
and taking an edge line with the crack probability less than or equal to the crack probability threshold as a second edge line.
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