Analysis method for porosity and pore size distribution of gypsum plaster board

Abstract

The invention discloses a method for analyzing porosity and pore size distribution of a gypsum plaster board, which comprises the following steps of: cutting the gypsum plaster board, painting a dark-color foaming agent on a cutting surface, and shooting to obtain an image of the cutting surface painted with the dark-color foaming agent for coloring; the cutting surface image is converted into a grayscale image, binarization processing is carried out based on a threshold value, a binary image represented by black and white pixels is obtained, the black pixels in the binary image represent cells in the gypsum plaster board core material, and the white pixels represent solid bases in the gypsum plaster board core material; the porosity calculation is carried out by using the number of black pixels as the cell area in the gypsum plaster board core material and the number of white pixels as the solid base area in the gypsum plaster board core material, so that the two-dimensional apparent porosity of the gypsum plaster board core material is obtained, then edge detection, searching and outline drawing are carried out, and the cell area, number and position data are obtained. And further obtaining a pore size distribution condition result of the cells. The porosity and pore size distribution condition of the gypsum plaster board can be rapidly and accurately analyzed through the convenient method, and the method is suitable for being applied to various occasions, easy to operate, convenient, rapid, efficient and low in cost.

Description

Technical Field

[0001] The invention belongs to the technical field of building material detection, and particularly relates to an analysis method for porosity and pore size distribution of a gypsum board. Background Art

[0002] Gypsum board is a building board made of building gypsum as the main raw material, mixed with admixtures, and hydrated in contact with water to form a core material, which is then firmly combined with the facing paper. On the one hand, because water is added in the process of making gypsum board, when the gypsum board is dry, the cavity originally occupied by water becomes a hole. On the other hand, in order to reflect the characteristics of lightness and give full play to the various superior properties brought by the lightness of gypsum board, admixtures are often added in the production process of gypsum board, among which the main admixtures include foaming agents, that is, air entraining agents. Therefore, holes produced by the action of foaming agents are left in the finished gypsum board, which are called bubbles. These intentionally produced bubbles are the main source of the numerous tiny cavities in the core material of gypsum board.

[0003] Lightweight is the characteristic of gypsum board. The light weight of gypsum board can improve the fireproof, heat preservation and thermal insulation properties of the board, and can also reduce the consumption of raw materials and production energy consumption. It can save transportation costs, simplify construction operations, and reduce the cost of buildings.

[0004] The lightweight advantage is firstly due to the specific gravity of the raw materials used to make gypsum board, and secondly, it depends on the optimization of the porosity and pore size distribution of the finished product determined by the production process. Therefore, it is necessary to study the porosity and pore size distribution of the core material of gypsum board to further improve the performance of gypsum board. The first step in the study of porosity and pore size distribution is to establish an analysis method.

[0005] At present, there are many methods for measuring the porosity of materials, and they are also relatively mature. These methods include: weight method, water immersion method, oil immersion method, water drainage method, gas method, resistance method, sound velocity method, mercury intrusion method, etc. These methods are not suitable for the detection of the porosity of the core material of gypsum board. In view of the fact that the materials that make up the gypsum board - gypsum and facing paper are not resistant to water and oil, and are non-conductive, and the material added value is low, therefore, the above porosity test methods are not suitable for the determination of the porosity of the core material of gypsum board.

[0006] In addition, the main means of detecting the pore size distribution of materials is currently the use of instrumental analysis methods, such as: specific surface area and pore size analyzers. However, the use of instrumental analysis methods has its disadvantages. First, the applicability of the method is questionable; second, the cost of instrumental analysis methods is very high; third, it requires a high level of manpower investment; fourth, the waste of liquid nitrogen resources used in instrumental analysis methods is serious; fifth, the prospect of instrumental analysis methods being widely promoted is not optimistic.

[0007] Therefore, how to provide a method for analyzing the porosity and pore size distribution of gypsum board quickly, conveniently and accurately is still one of the technical problems that technical personnel in this field urgently need to solve. Summary of the invention

[0008] In order to solve the shortcomings of the prior art and achieve the purpose of quickly, conveniently and accurately analyzing the porosity and pore size distribution of gypsum board, the present invention adopts the following technical solution:

[0009] A method for analyzing porosity and pore size distribution of gypsum board comprises the following steps:

[0010] Step S1: cutting the gypsum board, applying a dark-colored foaming agent on the cut surface and photographing the cut surface to obtain an image of the cut surface painted with the dark-colored foaming agent;

[0011] Step S2: Convert the cut surface image into a grayscale image, and perform binarization processing based on a threshold value to obtain a binary image expressed by black and white pixels, wherein the black pixels in the binary image represent the pores in the gypsum board core material, and the white pixels represent the solid base in the gypsum board core material. The number of black pixels refers to the pore area in the gypsum board core material, and the number of white pixels refers to the solid base area in the gypsum board core material. Porosity calculation is performed to obtain the two-dimensional apparent porosity of the gypsum board core material, and then edge detection, search and contour drawing are performed to obtain the pore area, number and position data, and then the pore size classification distribution results of the pores are obtained.

[0012] Since the detected edges are discontinuous, that is, the edges are not a whole area, the next step is to draw the contour through contour detection so that the edges are connected into a whole area for subsequent area calculation, counting, comparison, classification and other operations.

[0013] Furthermore, the binarization process in step S2 is to divide the pixels into black pixels and white pixels according to the grayscale values ​​of the pixels through a binarization threshold, and the binarization threshold is 97.

[0014] Since graphite black pore developer is used for coloring, the grayscale of the colored solid surface is darker than that of the uncolored solid surface, that is, the grayscale value is smaller. In order to make the watershed values ​​of black and white pixels extracted from the colored solid surface and the uncolored solid surface as consistent as possible, and to more accurately make the size and shape of the bubbles detected in the image the same as the actual bubbles, or as consistent as possible, it is necessary to adjust the threshold of the grayscale value to reduce the gray tolerance, that is, reduce the threshold. Thus, by reducing the threshold of binarization, it is suitable for the bubble detection method after the paper-faced gypsum board is colored;

[0015] In the prior art, 127 is usually used as the binarization threshold, but the present invention uses graphite powder, and a certain amount of graphite powder will also stick to the surface of the solid base during the painting process, and the cut surface of the sample cannot be completely flat. After being painted with black dye, the captured image will mistakenly identify the uneven solid base surface as bubbles. Therefore, selecting a binarization threshold below 127 can theoretically obtain a more objective binary image.

[0016] After many experiments, for different images, when the binarization threshold is 127, the error of the porosity compared to the calibrated porosity is a positive error, and the error is large. When the binarization threshold is 97, 82 and other values, the porosity error is a negative error. Theoretically, there will be an equivalence point between the positive and negative errors, that is, the error is 0, but since the equivalence points of different images are different, it is difficult to get an optimal equivalence point. Through experiments, it was accidentally found that when the binarization threshold is 97, a lower negative error value can be obtained, and the fluctuation between different images is small.

[0017] At the same time, when the binarization threshold is 96, the errors are all negative. The most important indicator for gypsum board is the fracture load. Under a certain porosity, the fracture load meets the detection requirements, indicating that the foundation of the gypsum board is solid, but the porosity of the positive error is falsely high, that is, the actual proportion of the foundation is larger, which leads to the overestimation of the foundation strength of the gypsum board. On the contrary, the negative error porosity will make the detected foundation strength of the gypsum board underestimated, but it only slightly (the negative error of 96 is very small) increases the detection requirements and will not cause unqualified products to pass the test.

[0018] On the other hand, the present invention analyzes the graded distribution of pore size, that is, the pores need to be graded in size, the purpose of which is to evaluate the foaming effect of the foaming agent; theoretically, the pores should be neither too large nor too small, that is, the size should be uniform, and the pores should be neither too many nor too few, that is, the number should be appropriate; therefore, the most ideal state is that all pores are of the same size and evenly distributed, however, in actual detection, due to the uneven distribution of pores, two or more pores will be connected to each other to form large pores, but some pores are not truly connected, but because there is a concave surface between two or more pores when the cross section is cut, and the graphite powder is attached to the concave surface between the pores during coloring, resulting in the captured image showing larger pores connected to each other; however, since the concave surface is usually shallow, even if the graphite powder is attached, its color will not be as dark as the pores, and lowering the binarization threshold to 97 can also separate pseudo-connected pores, thereby more realistically presenting pores of different levels, reflecting the real foaming effect of the foaming agent.

[0019] Furthermore, in step S2, the pixels of the blue, green and red channels are arranged in a matrix form and converted into grayscale values ​​by the following formula:

[0020] H=Pi×B+Pj×G+Pk×R

[0021] Among them, H represents the gray value, which is between 0 and 255; Pi represents the blue weight coefficient; B represents the pixel blue value; Pj represents the green weight coefficient; G represents the pixel green value; Pk represents the red weight coefficient; R represents the pixel red value;

[0022] The grayscale image is obtained by arranging the matrix with pixel grayscale values, and the grayscale image is binarized. The formula is as follows:

[0023]

[0024] Among them, g(x,y) represents the grayscale value of the pixel at the pixel coordinate (x,y) in the two-dimensional grayscale image; k represents the special threshold; b(x,y) represents the binary pixel black and white value at the pixel coordinate (x,y) in the two-dimensional grayscale image, 0 represents the black pixel, which represents the core material pores in the image, and 255 represents the white pixel, which represents the core material solid base in the image.

[0025] Furthermore, in step S2, the expression of porosity is:

[0026]

[0027] Wherein, r represents the two-dimensional apparent porosity, expressed as a percentage (%); S0 represents the pore area represented by the number of black pixels, and n represents the number of black pixels representing the pores; S 255 represents the solid base area indicated by the number of white pixels, and m represents the number of white pixels representing the solid base.

[0028] Furthermore, in step S2, the boundary between the pores and the solid base is identified using an edge detection algorithm, and the edge detection algorithm includes the following steps:

[0029] Step S2.6.1: Remove noise: Use Gaussian filter to smooth the image;

[0030] Step S2.6.2: Gradient calculation: Use the Sobel operator to calculate the first-order derivatives of the image in the horizontal and vertical directions, and identify the gradient and direction of the boundary on the two gradient images obtained;

[0031] Step S2.6.3: Non-maximum suppression: Scan the pixels one by one along the gradient direction and only identify the local maximum, so that the identified edge is infinitely close to the real boundary;

[0032] Step S2.6.4: Dual threshold detection: set two thresholds, a high threshold and a low threshold. If the gradient value of the edge pixel exceeds the high threshold, the pixel is marked as a strong edge; if the gradient value is between the high threshold and the low threshold, the pixel is marked as a weak edge; if the gradient value is lower than the low threshold, the edge pixel is discarded; the edge is determined by judging whether the weak edge is connected to the strong edge. If the weak edge is connected to the strong edge, the edge is determined to be an edge, otherwise, the edge is a weak edge and is discarded.

[0033] Furthermore, in step S2, the obtained cut surface image is preprocessed and smoothed using Gaussian filtering, and the formula is as follows:

[0034] G(x,y)=(1 / (2πσ 2 ))e (-(x2+y2) / (2σ2))

[0035] Among them, G(x,y) represents the value of the Gaussian filter function at the point (x,y); x and y represent the horizontal and vertical coordinates of the pixel point; e represents the natural exponent; σ represents the standard deviation of the Gaussian distribution, which determines the width and smoothness of the Gaussian function;

[0036] For the smoothed image, component information is extracted, morphological operations are performed, and the opening operation method is used to first erode to achieve element segmentation and remove burrs. Then the image is expanded, blanks are filled, and noise is removed to extract valuable contours and obtain the preprocessed image.

[0037] Furthermore, in step S1, the dark foaming agent is graphite powder, and the graphite powder with a fineness between 1200 mesh and 1800 mesh is applied to the cross-section of the core material of the gypsum board, and the cross-section is enlarged and photographed, with a magnification between 21 and 135 and a resolution of 1024×1280 or above, so as to obtain a more uniform and contrast-clear binary image later.

[0038] Furthermore, in step S1, the cutting surface is at least 200 mm away from the edge of the gypsum board, and is cut 50 mm longitudinally and 100 mm transversely along the gypsum board, so as to avoid the influence of irregular or uneven edges of the bubbles on the overall bubble detection.

[0039] Furthermore, in step S1, ring lighting is used for shooting, with an outer diameter of the ring being greater than 250 mm and a power being greater than 30 W. Due to the unevenness of the cutting surface, the enlarged protrusions are likely to block part of the cells, thereby affecting the final detection result.

[0040] Furthermore, a circle of white border is added around the cropped surface image, and the formed new image is used for subsequent edge detection to provide conditions for identifying black pixels at the edge of the original image.

[0041] The advantages and beneficial effects of the present invention are:

[0042] The present invention discloses a method for analyzing the porosity and pore size distribution of a gypsum board. The method extracts and calculates the pixels of cells and solid base of the gypsum board cross section after pretreatment, obtains the information of the porosity and pore size distribution of the cells, and detects the porosity and pore size distribution of the gypsum board more quickly and accurately by convenient means. The present invention is applicable to various occasions, is easy to operate, convenient and efficient, and has low cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of a method in an embodiment of the present invention.

[0044] Figure 2 It is the original picture taken in the embodiment of the present invention.

[0045] Figure 3 It is a new image with a border added to the original image in the embodiment of the present invention.

[0046] Figure 4 It is a binarized image when the threshold is 127 in the embodiment of the present invention.

[0047] Figure 5 This is a canny edge detection result diagram when the threshold is 127 in an embodiment of the present invention.

[0048] Figure 6 This is a diagram showing the effect of cell identification and marking when the threshold value is 127 in an embodiment of the present invention.

[0049] Figure 7 It is a schematic diagram of the output result when the threshold value is 127 in an embodiment of the present invention.

[0050] Figure 8 This is a canny edge detection result diagram when the threshold is 97 in an embodiment of the present invention.

[0051] Fig. 9 This is a diagram showing the effect of cell identification and marking when the threshold value is 97 in an embodiment of the present invention.

[0052] Fig.10 It is a schematic diagram of the output result when the threshold value is 97 in an embodiment of the present invention.

[0053] Fig.11 4 is a graph showing the relationship between the fine binarization threshold and the relative error in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0055] like Figure 1 As shown, a method for analyzing the porosity and pore size distribution of a gypsum board comprises the following steps:

[0056] Step S1: Image acquisition, including the following steps:

[0057] Step S1.1: Sample preparation step: Use a utility knife to cut the gypsum board to obtain a sample for analysis; specifically, use a utility knife to cut the sample. The sample is at least 200 mm away from the edge of the gypsum board and is cut along the gypsum board by 50 mm in the longitudinal direction and 100 mm in the transverse direction.

[0058] Step S1.2: Coloring step: Use a foaming agent to color the photographed surface of the gypsum board core material to enhance the appearance of bubbles in order to distinguish between the solid base and the bubbles; specifically, use a foaming agent to color the gypsum board core material to show the bubbles. The foaming agent is graphite powder with a fineness of 1200 to 1800 mesh. Use a brush dipped in graphite powder and lightly apply it to the long side of the sample and the cross-section of the gypsum board core material.

[0059] Step S1.3: Lighting step: Use lighting equipment to provide a suitable photographic environment to highlight the characteristics of the cells; specifically, create a bright photographic environment, annular lighting, use annular fluorescent lamps, tube diameter model T5, annular outer diameter 250mm, power 30w.

[0060] Step S1.4: Photographing step: photograph the core material of the gypsum board by photographing equipment; specifically, photograph the cross-section of the core material of the gypsum board by using an industrial microscope. The electronic magnification of the industrial microscope is 21 to 135, and the resolution is 1024×1280. Figure 2 shown.

[0061] Step S1.5: Image retouching step: retouching the image in pixel size to meet the effect of displaying the entire image on one screen; specifically, using image retouching software to crop the captured image so that the row pixels of the cropped image are between 1200 and 1300, and the column pixels are between 600 and 700.

[0062] Step S2: Analyze the image by image processing technology; specifically, use modern machine vision technology to analyze the effective information of the image, extract valuable data through analysis, and finally obtain the result data of porosity and pore size distribution, which specifically includes the following steps:

[0063] Step S2.1: The black pixels in the digital image represent the pores in the core material of the gypsum board, and the white pixels represent the solid base in the core material of the gypsum board; the total number of pixels refers to the area of ​​the core material of the gypsum board, the number of black pixels refers to the area of ​​the pores in the core material of the gypsum board, and the number of white pixels refers to the area of ​​the solid base in the core material of the gypsum board; a circle of white borders is added around the original image to form a new graphic to provide conditions for identifying the black pixels at the edge of the original image, such as Figure 3 shown.

[0064] Step S2.2: Image smoothing is performed to remove noise. Gaussian filtering is used. The formula for Gaussian filtering is:

[0065]

[0066] Among them, G(x,y) represents the value of the Gaussian filter function at the point (x,y); x and y represent the horizontal and vertical coordinates of the pixel point; e represents the natural exponent; σ represents the standard deviation of the Gaussian distribution, which determines the width and smoothness of the Gaussian function;

[0067] Extract component information, perform morphological operations, use the opening method, first erode to achieve element segmentation and remove burrs, then dilate the image, fill in the blanks, and remove noise.

[0068] Step S2.3: The image obtained from the industrial microscope is arranged in a matrix with pixels of the blue, green, and red channels in turn, and converted into a matrix arranged with the grayscale values ​​of the pixels through formula calculation. The conversion formula is:

[0069] H=Pi×B+Pj×G+Pk×R

[0070] Among them, H represents the grayscale value, which is between 0 and 255; Pi represents the blue weight coefficient; B represents the pixel blue value; Pj represents the green weight coefficient; G represents the pixel green value; Pk represents the red weight coefficient; and R represents the pixel red value.

[0071] Step S2.4: In a grayscale image, the grayscale value is any value in the continuous range of 0 to 255. To meet the statistical requirements of machine vision, the grayscale value is binarized, such as Figure 4 As shown, the black and white status of pixels are distinguished by the extreme values ​​0 or 255 at both ends to produce a binary image; the binary expression formula is:

[0072]

[0073] Among them, g(x,y) represents the grayscale value of the pixel at the pixel coordinate (x,y) in the two-dimensional image; k represents the special threshold; b(x,y) represents the binary pixel black and white value at the pixel coordinate (x,y) in the two-dimensional image, 0 represents the black pixel, which represents the core material pores in the image, and 255 represents the white pixel, which represents the core material solid base in the image.

[0074] Step S2.5: Based on step S2.4, the expression of porosity is:

[0075]

[0076] Wherein, r represents the two-dimensional apparent porosity, expressed as a percentage (%); S0 represents the pore area indicated by the number of black pixels in the digital image of the gypsum board core material, and n represents the number of black pixels representing the pores; S 255 represents the solid base area indicated by the number of white pixels in the digital image of the gypsum board core material, and m represents the number of white pixels representing the solid base;

[0077] Step S2.6: The boundary between the pores and the solid base is identified using the Canny edge detection algorithm, such as Figure 5 As shown, the Canny edge detection algorithm includes the following steps:

[0078] Step S2.6.1: Remove noise: Use Gaussian filter to smooth the image;

[0079] Step S2.6.2: Gradient calculation: Use the Sobel operator to calculate the first-order derivatives of the image in the horizontal and vertical directions, and identify the gradient and direction of the boundary on the two gradient images obtained;

[0080] Step S2.6.3: Non-maximum suppression: Scan the pixels one by one along the gradient direction and only identify the local maximum, so that the identified edge is infinitely close to the real boundary;

[0081] Step S2.6.4: Dual threshold detection: set two thresholds, a high threshold and a low threshold;

[0082] If the edge pixel gradient value exceeds the high threshold, the pixel is marked as a strong edge; if the gradient value is between the high threshold and the low threshold, the pixel is marked as a weak edge; if the gradient value is lower than the low threshold, the edge pixel is discarded; the edge is determined by judging whether the weak edge is connected to the strong edge. Specifically, if the weak edge is connected to the strong edge, the edge is determined as an edge, otherwise, the edge is a weak edge and is discarded. The marked cells are as follows Figure 6 shown.

[0083] The pores in the core material of the gypsum board represented by the black pixels in the digital image are graded and counted to reflect the pore size distribution. Two different statistical methods for the total area of ​​gypsum board pores are used to verify the correctness and accuracy of the statistical results.

[0084] Embodiment 1:

[0085] 1. Image acquisition

[0086] 1. Make samples

[0087] On the front of the gypsum board, use a utility knife to lean against the long side of the T-square to make a horizontal crease, then apply force to fold the gypsum board horizontally, and use the utility knife to cut through the protective paper at the fold on the back of the gypsum board to obtain a large sample. Using a similar method, use the utility knife to repeatedly cut on the front and back of the gypsum board, and cut a small sample 200mm away from the edge of the gypsum board, 50mm vertically and 100mm horizontally along the gypsum board, which is the sample for analysis.

[0088] 2. Sample coloring

[0089] Use a brush, dip it in pore developer, and apply it on the long side of the sample.

[0090] 3. Ambient lighting

[0091] Use circular fluorescent lights to create a suitable lighting environment.

[0092] 4. Image capture

[0093] Using an industrial microscope, the colored surface of the sample coated with the pore developer is photographed to obtain an image of the cross-section of the core material of the paper-faced gypsum board of the sample for analysis.

[0094] 5. Photo editing

[0095] Photoshop was used to edit the original image and the image size was cut into a new image with 1250 pixels for rows and 650 pixels for columns.

[0096] 2. Image Analysis

[0097] 1. Based on OpenCV-python machine vision software, a unique programming is used to analyze the acquired sample images. Software conditions are: OpenCV version number: 4.9.0.80; Python version number: 3.10.11.

[0098] 2. The analysis program code of porosity and pore size distribution of gypsum board mainly includes the following contents: reading image, adding border, Gaussian filtering, opening operation, graying, binarization, calculating porosity, edge detection, finding contour, drawing contour, marking contour, calculating pore size distribution, and outputting image and data.

[0099] 3. The output after the program runs is as follows Figure 7 As shown, the figure shows the porosity of the gypsum board sample and the number of cells at different pixel area levels and their proportion to the total number of cells.

[0100] Embodiment 2:

[0101] The threshold setting of binarization is a sensitive parameter of the target object detection rate. The grayscale value is in the range of 0-255, with 0 representing pure black and 255 representing pure white. For an image with a relatively balanced black and white, if binarization is required, in the prior art, the binarization threshold is usually set to an intermediate value, i.e. 127, because the total number from 0 to 127 is 128 (including 0), and the total number from 128 to 255 is also 128.

[0102] However, since the present invention uses graphite black pore developer for coloring, the grayscale of the colored solid surface is darker than that of the uncolored solid surface, that is, the grayscale value is smaller. Therefore, in order to make the watershed values ​​of the black and white pixels extracted from the colored solid surface and the uncolored solid surface as consistent as possible, and to more accurately make the size and shape of the bubbles detected in the image the same as the actual bubbles, or as consistent as possible, it is necessary to adjust the threshold of the gray value to reduce the gray tolerance, that is, reduce the threshold. Therefore, the present invention reduces the threshold of binarization to be suitable for detecting bubbles after the gypsum board is colored.

[0103] Specifically, based on the first embodiment, the binarization threshold is set to 97, that is, in the image analysis stage, in the analysis program code of the porosity and pore size distribution of the gypsum board, the binarization threshold segmentation is:

[0104] retval,threshold2=cv2.threshold(gray1,97,255,cv2.THRESH_BINARY)

[0105] Its pore recognition effect is as follows Figure 8 , Fig. 9 shown.

[0106] The determination of the binarization threshold value 97 of the present invention is based on the fact that after the blistering agent is applied and colored, the grayscale of the gypsum core material base deepens, causing the grayscale of the base surface to be darker than the grayscale of the uncolored base surface, that is, the grayscale value is smaller (the grayscale value is in the range of 0 to 255, 0 represents pure black, and 255 represents pure white). Therefore, in order to make the watershed values ​​of black and white pixels extracted from the colored base surface and the uncolored base surface as consistent as possible, it is necessary to adjust the threshold value of the grayscale value to reduce the grayscale tolerance, that is, the threshold value is reduced. Based on this, a wide range of binarization thresholds are first used for batch sample specimens to obtain the relative error between the detection result value of a certain threshold of binarization and the calibration value (see Table 1), and then the average value of all corresponding relative errors under a certain threshold is calculated (see Table 2). The observation results show that the average relative error in the binarization threshold range of 92 to 102 is small, so the range is narrowed (see Table 3) and each binarization threshold within this range is refined to conduct experiments one by one, thus obtaining the accurate result with the minimum average relative error (see Table 4), namely: 97 (see Fig.11 ).

[0107] Table 1 Relationship between broad binarization threshold and relative error

[0108]

[0109] Table 2 Average values ​​of wide relative errors

[0110]

[0111] Table 3 Relationship between fine binarization threshold and relative error

[0112]

[0113] Table 4 Average value of fine relative error

[0114]

[0115] On the other hand, the purpose of grading the pores is to evaluate the foaming effect of the foaming agent. Theoretically, the pores should be neither too many nor too few, that is, the number should be appropriate, and the pores should be neither too large nor too small, that is, the size should be moderate, and the pores should be symmetrical and evenly distributed. Therefore, manufacturers are working tirelessly to achieve the design goals. For this reason, it is particularly important to quickly, conveniently and accurately analyze the porosity and pore size distribution of gypsum board, thereby promoting the development of the building materials and decoration industry.

[0116] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing porosity and pore size distribution of gypsum board, characterized in that The steps include: Step S1: cutting the gypsum board, applying a dark-colored foaming agent on the cut surface and photographing the cut surface to obtain an image of the cut surface painted with the dark-colored foaming agent; Step S2: Convert the cut surface image into a grayscale image, and perform binarization processing based on a threshold value to obtain a binary image expressed by black and white pixels, wherein the black pixels in the binary image represent the pores in the gypsum board core material, and the white pixels represent the solid base in the gypsum board core material. The number of black pixels refers to the pore area in the gypsum board core material, and the number of white pixels refers to the solid base area in the gypsum board core material. Porosity calculation is performed to obtain the two-dimensional apparent porosity of the gypsum board core material, and then edge detection, search and contour drawing are performed to obtain the pore area, number and position data, and then the pore size grading distribution results of the pores are obtained.

2. The method for analyzing porosity and pore size distribution of gypsum board according to claim 1, characterized in that: The binarization process in step S2 is to divide the pixels into black pixels and white pixels according to the grayscale values ​​of the pixels through a binarization threshold, and the binarization threshold is 97.

3. The method for analyzing porosity and pore size distribution of gypsum board according to claim 1 or 2, characterized in that: In step S2, the pixels of the blue, green and red channels are arranged in a matrix and converted into grayscale values ​​by the following formula: H=Pi×B+Pj×G+Pk×R Among them, H represents the gray value; Pi represents the blue weight coefficient; B represents the pixel blue value; Pj represents the green weight coefficient; G represents the pixel green value; Pk represents the red weight coefficient; R represents the pixel red value; The grayscale image is obtained by arranging the matrix with pixel grayscale values, and the grayscale image is binarized. The formula is as follows: Among them, g(x,y) represents the grayscale value of the pixel at the pixel coordinate (x,y) in the two-dimensional grayscale image; k represents the special threshold; b(x,y) represents the binary pixel black and white value at the pixel coordinate (x,y) in the two-dimensional grayscale image, 0 represents the black pixel, which represents the core material pores in the image, and 255 represents the white pixel, which represents the core material solid base in the image.

4. The method for analyzing porosity and pore size distribution of gypsum board according to claim 1 or 2, characterized in that: In step S2, the boundary between the pores and the solid base is identified by using an edge detection algorithm, and the edge detection algorithm includes the following steps: Step S2.6.1: Remove noise: Use Gaussian filter to smooth the image; Step S2.6.2: Gradient calculation: Calculate the first-order derivatives of the image in the horizontal and vertical directions, and identify the gradient and direction of the boundary on the two obtained gradient images; Step S2.6.3: Non-maximum suppression: Scan pixels one by one along the gradient direction and only identify local maxima; Step S2.6.4: Dual threshold detection: set two thresholds, a high threshold and a low threshold. If the gradient value of the edge pixel exceeds the high threshold, the pixel is marked as a strong edge; if the gradient value is between the high threshold and the low threshold, the pixel is marked as a weak edge; if the gradient value is lower than the low threshold, the edge pixel is discarded; the edge is determined by judging whether the weak edge is connected to the strong edge. If the weak edge is connected to the strong edge, the edge is determined to be an edge, otherwise, the edge is a weak edge and is discarded.

5. The method for analyzing porosity and pore size distribution of gypsum board according to claim 1 or 2, characterized in that: In step S2, the expression of porosity is: Where r represents the two-dimensional apparent porosity; S0 represents the pore area represented by the number of black pixels, n represents the number of black pixels representing the pores; S 255 represents the solid base area indicated by the number of white pixels, and m represents the number of white pixels representing the solid base.

6. The method for analyzing porosity and pore size distribution of gypsum board according to claim 1 or 2, characterized in that: In step S2, the obtained cut surface image is preprocessed and smoothed using Gaussian filtering, and the formula is as follows: Among them, G(x,y) represents the value of the Gaussian filter function at the point (x,y); x and y represent the horizontal and vertical coordinates of the pixel point; e represents the natural exponent; σ represents the standard deviation of the Gaussian distribution, which determines the width and smoothness of the Gaussian function; For the smoothed image, component information is extracted, morphological operations are performed, and the opening operation method is used to first erode to achieve element segmentation and remove burrs. Then the image is expanded, blanks are filled, and noise is removed to obtain the preprocessed image.

7. The method for analyzing porosity and pore size distribution of gypsum board according to claim 1, characterized in that: In step S1, the dark foaming agent is graphite powder, and the graphite powder with a fineness between 1200 mesh and 1800 mesh is applied to the cross-section of the core material of the paper-faced gypsum board, and the cross-section is magnified and photographed, with a magnification between 21 and 135 and a resolution above 1024×1280.

8. The method for analyzing porosity and pore size distribution of gypsum board according to claim 1, characterized in that: In the step S1, the cutting surface is at least 200 mm away from the edge of the gypsum board, and is cut along the gypsum board by 50 mm in the longitudinal direction and 100 mm in the transverse direction.

9. The method for analyzing porosity and pore size distribution of gypsum board according to claim 1, characterized in that: In step S1, ring lighting is used for shooting, with an outer diameter of the ring being greater than 250 mm and a power being greater than 30 W.

10. The method for analyzing porosity and pore size distribution of gypsum board according to claim 1, characterized in that: A circle of white border is added around the cropped surface image, and the formed new image is used for subsequent edge detection.

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