Concrete crack width image measurement method

Abstract

The invention particularly relates to a concrete crack width image measurement method, which comprises the following steps of: 1, shooting a concrete crack in a real environment to obtain an original image; 2, performing image processing on the original image to obtain a binary image; step 3, calculating the width of the concrete crack according to the binary image, adjusting image processing parameters until the difference between the calculated width of the concrete crack and the actually measured width of the concrete crack is within an allowable range, and storing the image processing parameters at the moment as concrete crack image processing parameters; and 4, performing concrete crack image processing according to the concrete crack image processing parameters, and calculating the width of the concrete crack. The invention further relates to a concrete crack width image measuring device, computer equipment and a storage medium. According to the invention, accurate extraction of the concrete crack binary image and accurate calculation of the concrete crack width based on the concrete crack binary image are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete crack width image measurement, and in particular to a concrete crack width image measurement method, device, computer equipment and storage medium. Background Art

[0002] With the popularization of image recognition algorithms and high-precision camera equipment, it is now possible to automatically identify concrete cracks based on pictures taken by ordinary high-definition cameras. The accuracy of image-based concrete crack detection results depends on how to extract binary images of concrete cracks from complex real-world photos, and how to ensure that the concrete crack segmentation edges are clear and accurate during the extraction process. The existing image-based concrete crack detection technology mainly uses neural networks to learn and train a large number of photos containing concrete cracks, and then determines whether the picture contains concrete cracks and locates the position of the concrete cracks in the picture. For example, the patent with publication number CN114705689A discloses a method and system for detecting cracks on the facade of a building based on a drone, and specifically discloses the construction of a crack image training data set and a verification data set based on photos acquired by the drone, training a semantic segmentation model based on multi-feature regional attention based on a convolutional neural network, and finally processing the crack binary image to calculate the actual length of the crack; the patent with publication number CN114111602A discloses a method for calculating the width of cracks on the surface of a bridge based on image technology, and specifically discloses a fitting method for establishing three single-pixel width curves of the upper edge, skeleton and lower edge of the crack for the binary image of the crack, and a method for calculating the width found on the crack skeleton curve. This method solves the problem of crack width calculation based on the crack binary image, but does not solve the problem of obtaining the crack binary image.

[0003] In summary, there is currently no general processing technology for accurately extracting binary images of concrete cracks from original images in real complex environments to calculate the width of concrete cracks. Summary of the invention

[0004] The purpose of the present invention is to provide a method for measuring the width of concrete cracks by performing image processing on the original image, thereby accurately extracting the binary image of concrete cracks and accurately calculating the width of concrete cracks based on the binary image of concrete cracks while accurately retaining the crack details in the original image.

[0005] In order to achieve the above object, the present invention provides a method for measuring concrete crack width image, comprising the following steps:

[0006] Step 1, photographing concrete cracks in a real environment to obtain original images;

[0007] Step 2: Perform image processing on the original image to obtain a binary image;

[0008] Step 3, calculating the concrete crack width according to the binary image, adjusting the image processing parameters until the difference between the calculated concrete crack width and the measured concrete crack width is within the allowable range, and saving the image processing parameters at this time as the concrete crack image processing parameters;

[0009] Step 4: Perform concrete crack image processing according to concrete crack image processing parameters to calculate the concrete crack width.

[0010] In the present invention, in step 2, image processing is performed on the original image to obtain a binary image, which includes the following steps:

[0011] Step 21, graying the original image to obtain a first grayscale image;

[0012] Step 22: filtering and enhancing the first grayscale image to obtain a second grayscale image;

[0013] Step 23: Perform threshold segmentation on the second grayscale image to obtain a binary image.

[0014] In the present invention, in step 21, the original image is grayed by a gray processing algorithm; the gray processing algorithm is preferably a weighted average algorithm; the weighted average algorithm grays the original image according to the following formula:

[0015] g(i,j)=0.3R(i,j)+0.59G(i,j)+0.11B(i,j)

[0016] Among them, g(i,j) is the grayscale value at i and j in the first grayscale image matrix, R(i,j) is the red pixel value at i and j in the original image matrix; G(i,j) is the green pixel value at i and j in the original image matrix; B(i,j) is the blue pixel value at i and j in the original image matrix.

[0017] In the present invention, in step 22, the first grayscale image is filtered by a filtering algorithm; the filtering algorithm is preferably an adaptive median filtering algorithm; the adaptive median filtering algorithm is used to filter the first grayscale image, including the following steps:

[0018] Sort all the pixels in the neighborhood of the central pixel of the filter window according to the grayscale value, and use the median grayscale value of all the pixels in the neighborhood of the central pixel of the filter window as the grayscale value of the central pixel of the filter window;

[0019] If the number of pixels in the neighborhood is an odd number, the grayscale value of the middle pixel in the neighborhood is used to replace the grayscale value of the center pixel of the filter window; otherwise, the average grayscale value of the two middle pixels is calculated to replace the grayscale value of the center pixel of the filter window.

[0020] In the present invention, the adaptive median filtering algorithm outputs the grayscale value of the center pixel of the filter window through the following steps:

[0021] Step A, A1 = Z med -Z min , A2=Z med -Z max ; If A1>0 and A2<0, jump to step B; otherwise, increase the filter window size;

[0022] If the increased filter window size ≤ S max , repeat step A; otherwise, use Z med As S xy Gray value of the center pixel;

[0023] Step B, B1 = Z xy -Z min , B2=Z xy -Z max ; If B1>0 and B2<0, then Z is still used xy As S xy The gray value of the center pixel; otherwise, Z med As S xy Gray value of the center pixel;

[0024] Among them, Z min For S xy The smallest gray value in max For S xy The maximum gray value in Z med For S xy The median of all gray values ​​in Z xy is the gray value of the pixel at the yth row and xth column in the first gray image; S max For S xy The maximum allowed window size; S xy is the filter window, and the center pixel of the filter window is the pixel in the yth row and the xth column in the first grayscale image.

[0025] In the present invention, in step 22, the first grayscale image is enhanced by an enhancement algorithm; the enhancement algorithm is preferably a piecewise linear enhancement algorithm in grayscale transformation; the grayscale transformation is to stretch the contrast within the interest range of the first grayscale image and reduce the contrast of the background through a set transformation mapping method, thereby achieving image enhancement; the piecewise linear enhancement is to transform the small concrete crack grayscale interval (r1, r2) in the first grayscale image into a large concrete crack grayscale interval (s1, s2) through a piecewise function, thereby highlighting the concrete cracks; the concrete crack grayscale interval (s1, s2) is (90, 180).

[0026] In this embodiment, in step 23, the threshold segmentation is to convert pixel points greater than the segmentation threshold into white points, and pixel points less than the segmentation threshold into black points;

[0027] The second grayscale image is threshold segmented according to the following formula:

[0028]

[0029] Wherein, T(x, y) is the second grayscale image threshold segmentation function, (x, y) is the pixel coordinates in the second grayscale image, T is the segmentation threshold, and D(x, y) is the grayscale value of the pixel in the second grayscale image.

[0030] In the present invention, the value range of the segmentation threshold is (160-170).

[0031] In the present invention, in step 3, the concrete crack width is calculated according to the number of pixels in the binary image; the calculated concrete crack width is compared with the measured concrete crack width;

[0032] When the difference between the calculated concrete crack width and the measured concrete crack width is within the allowable range, it is determined that the image processing parameter values ​​of steps 1-4 are reasonable, and the image processing parameters of steps 1-4 are saved as concrete crack image processing parameters for concrete crack image processing;

[0033] When the difference between the calculated concrete crack width and the measured concrete crack width is outside the allowable error range, the image processing parameters of steps 1-4 are readjusted until the difference between the calculated concrete crack width and the measured concrete crack width is within the allowable error range. The readjusted image processing parameters of steps 1-4 are saved as concrete crack image processing parameters for concrete crack image processing.

[0034] In the present invention, the concrete cracks are concrete cracks on the surface of the containment vessel of a nuclear power plant.

[0035] In order to achieve the above object, the present invention also provides a concrete crack width image measuring device, comprising:

[0036] An acquisition module is used to acquire original images taken under real conditions of concrete cracks;

[0037] The first image processing module is used to perform image processing on the original image to obtain a binary image; calculate the concrete crack width according to the binary image, adjust the image processing parameters until the difference between the calculated concrete crack width and the measured concrete crack width is within an allowable range, and save the image processing parameters at this time as concrete crack image processing parameters;

[0038] The second image processing module is used to perform concrete crack image processing according to concrete crack image processing parameters to calculate the concrete crack width.

[0039] In order to achieve the above object, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the above-mentioned concrete crack width image measurement method when executing the computer-readable instructions.

[0040] In order to solve the above technical problem, the present invention provides a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed, the steps of the above-mentioned concrete crack width image measurement method are implemented.

[0041] Beneficial technical effects of the present invention:

[0042] The concrete crack width image measurement method, device, computer equipment and storage medium of the present invention perform grayscale processing, adaptive median filtering and piecewise linear enhancement operations on the original image of the concrete crack, and gradually optimize the parameter selection of each operation according to the accuracy of the concrete crack width image measurement. Under the premise of accurately retaining the crack details in the original image of the concrete crack, accurate extraction of the concrete crack binary image and accurate measurement of the concrete crack width based on the concrete crack binary image are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of an embodiment of a method for measuring concrete crack width images of the present invention;

[0044] Figure 2 This is the principle diagram of the piecewise linear enhancement algorithm for concrete crack grayscale images;

[0045] Figure 3 The figure is a schematic structural view of an embodiment of a device for measuring concrete crack width according to the present invention.

[0046] Figure 4 The figure is a schematic structural view of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of this application; the terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0048] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0049] The technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings and specific embodiments.

[0050] refer to Figure 1 , a flow chart of an embodiment of a method for measuring the width of a concrete crack image is shown, the method for measuring the width of a concrete crack image comprising the following steps:

[0051] Step 1, photographing concrete cracks in a real environment to obtain original images;

[0052] Step 2: Perform image processing on the original image to obtain a binary image;

[0053] Step 3, calculating the concrete crack width according to the binary image, adjusting the image processing parameters until the difference between the calculated concrete crack width and the measured concrete crack width is within the allowable range, and saving the image processing parameters at this time as the concrete crack image processing parameters;

[0054] Step 4: Perform concrete crack image processing according to concrete crack image processing parameters to calculate the concrete crack width.

[0055] In this embodiment, in step 2, image processing is performed on the original image to obtain a binary image, including the following steps:

[0056] Step 21, graying the original image to obtain a first grayscale image;

[0057] Step 22: filtering and enhancing the first grayscale image to obtain a second grayscale image;

[0058] Step 23: Perform threshold segmentation on the second grayscale image to obtain a binary image.

[0059] In this embodiment, in step 21, the original image is grayed by a gray processing algorithm; the gray processing algorithm is preferably a weighted average algorithm, which performs weighted average of pixel values ​​for each gray value in the original image matrix according to different weight coefficients adjusted according to the importance of each color channel, so as to retain the original image information as much as possible and simulate the observation effect of the human eye;

[0060] The human eye is least sensitive to blue, and the weight coefficient of the blue channel in the original image is 0.11; the human eye is most sensitive to green, and the weight coefficient of the green channel in the original image is 0.59; the human eye is moderately sensitive to red, and the weight coefficient of the red channel in the original image is 0.3;

[0061] The weighted average algorithm grayscales the original image according to the following formula:

[0062] g(i,j)=0.3R(i,j)+0.59G(i,j)+0.11B(i,j)

[0063] Among them, g(i,j) is the grayscale value at i and j in the first grayscale image matrix, R(i,j) is the red pixel value at i and j in the original image matrix; G(i,j) is the green pixel value at i and j in the original image matrix; B(i,j) is the blue pixel value at i and j in the original image matrix.

[0064] In this embodiment, in step 22, filtering and enhancing the first grayscale image are a pair of opposite operations; filtering the first grayscale image will eliminate the noise of the first grayscale image, but will also eliminate the fine features of the concrete cracks; enhancing the first grayscale image will increase the noise of the first grayscale image, but will also highlight the fine features of the concrete cracks.

[0065] In this embodiment, in step 22, the first grayscale image is filtered by a filtering algorithm; the filtering algorithm is preferably an adaptive median filtering algorithm, which automatically adjusts the filter window size according to the neighborhood noise density. At the same time, when noise is detected in the neighborhood, the median grayscale value of the neighborhood pixel point is used as the grayscale value of the center pixel point of the filter window; when no noise is detected in the neighborhood, the grayscale value of the center pixel point of the filter window is processed.

[0066] In this embodiment, in step 22, the adaptive median filtering algorithm filters the first grayscale image, including the following steps:

[0067] Sort all the pixels in the neighborhood of the central pixel of the filter window according to the grayscale value, and use the median grayscale value of all the pixels in the neighborhood of the central pixel of the filter window as the grayscale value of the central pixel of the filter window;

[0068] If the number of pixels in the neighborhood is an odd number, the grayscale value of the middle pixel in the neighborhood is used to replace the grayscale value of the center pixel of the filter window; otherwise, the average grayscale value of the two middle pixels is calculated to replace the grayscale value of the center pixel of the filter window.

[0069] In this embodiment, the adaptive median filtering algorithm outputs the grayscale value of the center pixel of the filter window through the following steps:

[0070] Step A, A1 = Z med -Z min , A2=Z med -Z max ; If A1>0 and A2<0, jump to step B; otherwise, increase the filter window size;

[0071] If the increased filter window size ≤ S max , repeat step A; otherwise, use Z med As S xy Gray value of the center pixel;

[0072] Step B, B1 = Z xy -Z min , B2=Z xy -Z max ; If B1>0 and B2<0, then Z is still used xy As S xy The gray value of the center pixel; otherwise, Z med As S xy Gray value of the center pixel;

[0073] Among them, Z min For S xy The smallest gray value in max For S xy The maximum gray value in Z med For S xy The median of all gray values ​​in Z xy is the gray value of the pixel at the yth row and xth column in the first gray image; S max For S xy The maximum allowed window size; S xy is the filter window, and the center pixel of the filter window is the pixel in the yth row and the xth column in the first grayscale image.

[0074] See also Figure 2In this embodiment, in step 22, the first grayscale image is enhanced by an enhancement algorithm; the enhancement algorithm is preferably a piecewise linear enhancement algorithm in grayscale transformation; the grayscale transformation is to stretch the contrast within the interest range of the first grayscale image to a wider range and reduce the contrast of the background through a set transformation mapping method, thereby achieving image enhancement; the piecewise linear enhancement is to transform the small concrete crack grayscale interval (r1, r2) in the first grayscale image into a large concrete crack grayscale interval (s1, s2) through a piecewise function to achieve prominent display of the concrete cracks, and the concrete crack grayscale interval (s1, s2) is (90, 180).

[0075] In this embodiment, in step 23, the threshold segmentation is to convert pixel points greater than the segmentation threshold into white points, and pixel points less than the segmentation threshold into black points;

[0076] The second grayscale image is threshold segmented according to the following formula:

[0077]

[0078] Wherein, T(x, y) is the second grayscale image threshold segmentation function, (x, y) is the pixel coordinates in the second grayscale image, T is the segmentation threshold, and D(x, y) is the grayscale value of the pixel in the second grayscale image.

[0079] In this embodiment, the value range of the segmentation threshold is (160-170).

[0080] In this embodiment, in step 3, the concrete crack width is calculated according to the number of pixels in the binary image; the calculated concrete crack width is compared with the measured concrete crack width;

[0081] When the difference between the calculated concrete crack width and the measured concrete crack width is within the allowable range, it is determined that the image processing parameter values ​​of steps 1-4 are reasonable, and the image processing parameters of steps 1-4 are saved as concrete crack image processing parameters for concrete crack image processing;

[0082] When the difference between the calculated concrete crack width and the measured concrete crack width is outside the allowable error range, the image processing parameters of steps 1-4 are readjusted until the difference between the calculated concrete crack width and the measured concrete crack width is within the allowable error range. The readjusted image processing parameters of steps 1-4 are saved as concrete crack image processing parameters for concrete crack image processing.

[0083] In this embodiment, the concrete cracks are concrete cracks on the surface of the containment vessel of a nuclear power plant.

[0084] refer to Figure 2As an implementation of the above method, the present invention provides an embodiment of a concrete crack width image measuring device. The device embodiment corresponds to the embodiment of the above concrete crack width image measuring method. The device can be specifically applied to various electronic devices.

[0085] The concrete crack width image measuring device described in this embodiment includes:

[0086] An acquisition module is used to acquire original images taken under real conditions of concrete cracks;

[0087] The first image processing module is used to perform image processing on the original image to obtain a binary image; calculate the concrete crack width according to the binary image, adjust the image processing parameters until the difference between the calculated concrete crack width and the measured concrete crack width is within an allowable range, and save the image processing parameters at this time as concrete crack image processing parameters;

[0088] The second image processing module is used to perform concrete crack image processing according to concrete crack image processing parameters to calculate the concrete crack width.

[0089] See also Figure 4 As an implementation of the above method, the present invention provides an embodiment of a computer device, and the computer device embodiment corresponds to the embodiment of the above concrete crack width image measurement method.

[0090] The computer device described in this embodiment includes a memory, a processor and a network interface that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device with a memory, a processor and a network interface, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit, a programmable gate array, a digital processor, an embedded device, etc.

[0091] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0092] The memory includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory, random access memory, static random access memory, read-only memory, electrically erasable programmable read-only memory, programmable read-only memory, magnetic memory, disk, optical disk, etc. In some embodiments, the memory can be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory can also be an external storage device of the computer device, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc. equipped on the computer device. Of course, the memory can also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is generally used to store the operating system and various application software installed on the computer device, such as the computer-readable instructions of the above-mentioned concrete crack width image measurement method, etc. In addition, the memory can also be used to temporarily store various types of data that have been output or are to be output.

[0093] In some embodiments, the processor may be a central processing unit, a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is generally used to control the overall operation of the computer device. In this embodiment, the processor is used to run the computer-readable instructions stored in the memory or process data, such as running the computer-readable instructions of the above-mentioned concrete crack width image measurement method.

[0094] The network interface may include a wireless network interface or a wired network interface, which is generally used to establish a communication connection between the computer device and other electronic devices.

[0095] As an implementation of the above method, the present invention provides an embodiment of a computer-readable storage medium, and the embodiment of the computer-readable storage medium corresponds to the embodiment of the above concrete crack width image measurement method.

[0096] The computer-readable storage medium described in this embodiment stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the above-mentioned concrete crack width image measurement method.

[0097] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for measuring concrete crack width image, characterized in that: The following steps are involved: Step 1, photographing concrete cracks in a real environment to obtain original images; Step 2: Perform image processing on the original image to obtain a binary image; Step 3, calculating the concrete crack width according to the binary image, adjusting the image processing parameters until the difference between the calculated concrete crack width and the measured concrete crack width is within the allowable range, and saving the image processing parameters at this time as the concrete crack image processing parameters; Step 4: Perform concrete crack image processing according to concrete crack image processing parameters to calculate the concrete crack width.

2. The method for measuring concrete crack width image according to claim 1, characterized in that: In step 2, the original image is processed to obtain a binary image, which includes the following steps: Step 21, graying the original image to obtain a first grayscale image; Step 22: filtering and enhancing the first grayscale image to obtain a second grayscale image; Step 23: Perform threshold segmentation on the second grayscale image to obtain a binary image.

3. The method for measuring concrete crack width image according to claim 2, characterized in that: The original image is grayed out by a gray processing algorithm; the gray processing algorithm is a weighted average algorithm; The weighted average algorithm grayscales the original image according to the following formula: g(i,j)=0.3R(i,j)+0.59G(i,j)+0.11B(i,j) Wherein, g(i,j) is the grayscale value at i and j in the first grayscale image matrix, R(i,j) is the red pixel value at i and j in the original image matrix; G(i,j) is the green pixel value at i and j in the original image matrix; B(i,j) is the blue pixel value at position i, j in the original image matrix.

4. The method for measuring concrete crack width image according to claim 2, characterized in that: In step 22, the first grayscale image is filtered by a filtering algorithm; the filtering algorithm is an adaptive median filtering algorithm; the adaptive median filtering algorithm is used to filter the first grayscale image, including the following steps: Sort all the pixels in the neighborhood of the central pixel of the filter window according to the grayscale value, and use the median grayscale value of all the pixels in the neighborhood of the central pixel of the filter window as the grayscale value of the central pixel of the filter window; If the number of pixels in the neighborhood is an odd number, the grayscale value of the middle pixel in the neighborhood is used to replace the grayscale value of the center pixel of the filter window; otherwise, the average grayscale value of the two middle pixels is calculated to replace the grayscale value of the center pixel of the filter window.

5. The method for measuring concrete crack width image according to claim 4, characterized in that: The adaptive median filtering algorithm outputs the grayscale value of the center pixel of the filter window through the following steps: Step A, A1 = Z med -Z min , A2=Z med -Z max ; If A1>0 and A2<0, jump to step B; otherwise, increase the filter window size; If the increased filter window size ≤ S max , repeat step A; otherwise, use Z med As S xy Gray value of the center pixel; Step B, B1 = Z xy -Z min , B2=Z xy -Z max ; If B1>0 and B2<0, then Z is still used xy As S xy The gray value of the center pixel; otherwise, Z med As S xy Gray value of the center pixel; Among them, Z min For S xy The smallest gray value in max For S xy The maximum gray value in Z med For S xy The median of all gray values ​​in Z xy is the gray value of the pixel at the yth row and xth column in the first gray image; S max For S xy The maximum allowed window size; S xy is the filter window, and the center pixel of the filter window is the pixel in the yth row and the xth column in the first grayscale image.

6. The method for measuring concrete crack width image according to claim 2, characterized in that: In step 22, the first grayscale image is enhanced by an enhancement algorithm; the enhancement algorithm is a piecewise linear enhancement algorithm in grayscale transformation.

7. The method for measuring concrete crack width image according to claim 2, characterized in that: In step 23, the threshold segmentation is to convert pixel points greater than the segmentation threshold into white points, and pixel points less than the segmentation threshold into black points; The second grayscale image is threshold segmented according to the following formula: Wherein, T(x, y) is the second grayscale image threshold segmentation function, (x, y) is the pixel coordinates in the second grayscale image, T is the segmentation threshold, and D(x, y) is the grayscale value of the pixel in the second grayscale image.

8. A concrete crack width image measuring device, characterized in that: include: An acquisition module is used to acquire original images taken under real conditions of concrete cracks; The first image processing module is used to perform image processing on the original image to obtain a binary image; Calculate the concrete crack width according to the binary image, adjust the image processing parameters until the difference between the calculated concrete crack width and the measured concrete crack width is within an allowable range, and save the image processing parameters at this time as concrete crack image processing parameters; The second image processing module is used to perform concrete crack image processing according to concrete crack image processing parameters to calculate the concrete crack width.

9. A computer device comprising a memory and a processor, wherein the memory stores computer-readable instructions, characterized in that: When the processor executes the computer-readable instructions, the steps of the concrete crack width image measurement method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having computer-readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed, the steps of the concrete crack width image measurement method according to any one of claims 1 to 7 are implemented.

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