A method for rapid testing of two-dimensional shape characteristics of aggregate

Abstract

This invention provides a method for rapidly testing the two-dimensional shape characteristics of aggregates, relating to the field of civil engineering materials. The method includes: sieving to obtain a sufficient quantity of aggregates with a specified particle size; acquiring image information of the aggregates using an image acquisition device; processing the aggregate images using grayscale conversion, threshold segmentation, and binarization methods to facilitate subsequent calculations; obtaining the corresponding isoellipse information of the aggregate images by calculating the covariance matrix, further obtaining information such as the aspect ratio of the isoellipses; and calculating the perimeter and area of ​​polygons using contour edge recognition methods and Green's formula to obtain roundness values. This method can not only process aggregate information in large batches simultaneously to obtain the key two-dimensional shape characteristics of the aggregates in that batch, but also has advantages such as simple operation, short testing cycle, and accurate results, providing an efficient and convenient technical means for analyzing the impact of aggregate morphology on the performance of cement concrete and asphalt concrete.

Description

Technical Field

[0001] This invention relates to the field of civil engineering materials, and more specifically to a method for rapidly testing the two-dimensional shape characteristics of aggregates. Background Technology

[0002] Aggregates, as an indispensable component of asphalt and cement concrete, play a crucial role in the overall performance of concrete. Specifically, the shape, edges, and texture of aggregate particles affect the interactions between particles and with other materials (such as asphalt, cement, and lime), thereby influencing the durability, shear strength, tensile strength, and stiffness of asphalt concrete pavement surface layers, base layers, and cement concrete. Therefore, the accurate testing and quantification of aggregate shape characteristics are of great significance for controlling the quality of aggregate processing and production, as well as improving the performance of asphalt or cement concrete.

[0003] However, for a long time, the collection and analysis of aggregate shape characteristics have mainly relied on traditional testing methods, namely, judging aggregate shape by manual visual observation or simple measuring tools. This approach suffers from drawbacks such as low efficiency and inability to comprehensively quantify the shape characteristics of aggregates. For example, determining the content of needle-like and flaky aggregates is difficult to achieve accurately through manual testing, and excessive needle-like and flaky aggregates can significantly weaken the workability and mechanical properties of the mixture. Manual testing is even more difficult to implement for other descriptions of aggregate shape and is easily influenced by subjective human factors, leading to significant experimental biases. Furthermore, the time-consuming and labor-intensive nature of traditional methods greatly hinders the accurate measurement of aggregate shape characteristics.

[0004] With the rapid development of digital technology, significant progress has been made in fields such as computer vision and image processing. In the field of aggregate research, aggregate information acquisition technology has emerged. The widespread use of high-resolution imaging equipment, such as industrial-grade high-definition cameras and microscope cameras, can clearly capture image information of aggregate particles. Aggregate information acquisition methods can be divided into two-dimensional acquisition and three-dimensional acquisition. Three-dimensional information is more comprehensive, but this method is costly, the digital image processing is relatively complex, and its widespread adoption is difficult. In contrast, two-dimensional acquisition methods are more time-saving and convenient, and can quickly obtain key information about the shape characteristics of aggregates. However, there is still a lack of unified processing and analysis methods for accurately extracting key features from the acquired image information to obtain specific shape indicators.

[0005] Patent CN117929211A discloses a method for evaluating the three-dimensional morphological characteristics of coarse aggregate particles, which divides shotcrete coarse aggregates of different particle sizes into regions and classifies them by shape. However, this method only evaluates the aggregates based on particle size and axial ratio, neglecting the differences in evaluation results that may be caused by other key indicators. Patent CN106780457A calculates aggregate sphericity index, angularity index, and texture index using AIMS, but does not involve the calculation of roundness, aspect ratio of equivalent ellipse, etc. The only two-dimensional indicator is the angularity index, which is insufficient to comprehensively evaluate the two-dimensional shape characteristics of the aggregates. Patent CN117740675A discloses an aggregate morphology analysis device and method based on line scanning, which can realize the automated processing of large batches of aggregate samples. However, this method cannot accurately segment and identify each aggregate on the conveyor belt, and the evaluation parameter is the sphericity parameter, which does not consider shape parameters such as angularity, which also affect the performance of asphalt or cement concrete. Patent CN105136622A discloses a method for testing the flatness of coarse aggregates. This method involves capturing images of a batch of aggregates with a camera, processing the images using Image-Pro Plus, and calculating the ratio of the major and minor axes of each aggregate. While this method can process multiple aggregates at once, the camera's shooting accuracy is relatively low compared to image acquisition design, and it cannot accurately calculate other two-dimensional shape features, thus having limitations. Patent CN118052117A discloses a method for estimating aggregate porosity based on two-dimensional images. This method calculates the two-dimensional shape features of the aggregates, including roundness, convexity, aspect ratio, and angularity. Its focus is on exploring the estimation method of aggregate porosity, without detailing the processing method of the two-dimensional images and the calculation method of shape features. In contrast, this invention uses the aspect ratio of an equivalent ellipse as the shape feature, rather than directly calculating the aspect ratio based on the major and minor axes of the aggregates. The equivalent ellipse has the same first and second moments as the original aggregate, making its shape feature more equivalent to the original aggregate, resulting in a more reasonable result. Patent CN119131259A discloses a method for characterizing the three-dimensional morphological features of coarse aggregates. This method can calculate the shape, edges, and texture of coarse aggregates, but the calculation is relatively complex. It requires testing with a three-dimensional laser scanner, which places high demands on the equipment. Furthermore, it can only test the aggregate features one by one in sequence. For a large number of samples, the workload is large and time-consuming. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method for rapidly testing the two-dimensional shape characteristics of aggregates. This method enables rapid testing of aggregate shape characteristics and has advantages such as simple operation, high testing efficiency, and high accuracy of results. It provides a fast and efficient technical means for exploring the influence of aggregate shape characteristics on the performance of asphalt concrete, cement concrete, and inorganic binder stabilized layers.

[0007] To achieve the above objectives, the technical solution of the present invention is implemented through the following technical solution:

[0008] A method for rapidly testing the two-dimensional shape characteristics of aggregates, the method comprising the following steps:

[0009] S1. Clean the aggregate, dry it, and then cool it to room temperature;

[0010] S2. Use an aggregate image measurement system to acquire aggregate image information;

[0011] S3. Process the aggregate image: including grayscale conversion, threshold segmentation, image binarization, and image inversion, to obtain the processed aggregate image;

[0012] S4. Locate the aggregate polygons in the processed aggregate image and obtain the polygon boundaries;

[0013] S5. Obtain the centroid of the aggregate polygon, calculate the first and second moment parameters, and obtain the major and minor axis information of the isoellipse through covariance.

[0014] S6. Calculate the area and perimeter of the aggregate polygon;

[0015] S7. Calculate the major axis length, minor axis length, angularity, roundness, and aspect ratio of the equivalent ellipse of all test aggregates to obtain comprehensive two-dimensional shape characteristic indicators of the aggregates.

[0016] Preferably, the aggregate particle size range used in step S1 is 0.075mm-26.5mm.

[0017] Preferably, in step S2, an aggregate image measurement system is used to acquire aggregate image information. After the test, two-dimensional shape index data including the major axis length, minor axis length, and angularity of the aggregate can be directly obtained, and an aggregate image with the file extension .tif can be obtained.

[0018] Preferably, in step S3, the image is binarized using an algorithm, and the polygon is set as the foreground, i.e., the polygon value is 1 and the background value is 0.

[0019] Preferably, step S4 employs the Canny edge detection algorithm to read multiple aggregate image information from a single image at once. The specific implementation process is as follows:

[0020] S4-1. Use Gaussian filtering to reduce noise in the image. In Gaussian smoothing, pixels at different positions are assigned different weights, so that neighboring pixels have larger weight values. Use Gaussian filtering with kernel size of 3*3 or 5*5 for processing. The kernel size must be N*N and be an odd number greater than 1.

[0021] S4-2. The Sobel operator is used to calculate the gradient of the image. The convolution template is applied to the horizontal and vertical directions respectively to obtain the calculated gradient value and direction. The gradient direction can be divided into four directions: 0°, 45°, 90° and 135°.

[0022] S4-3. A non-maximum suppression strategy is adopted. Among the pixels that may be edges, only the pixels with the largest local gradient are retained, and the edge responses corresponding to the non-maximum pixels are suppressed to 0, thus obtaining more accurate edges with a single pixel width. When the gradient direction is 45°, the detection formula is:

[0023]

[0024] The edges of the aggregate polygons are then determined based on dual threshold detection, where the high threshold is set to 25% of the peak gradient amplitude and the low threshold is set to 40% of the high threshold, i.e., 10% of the peak gradient amplitude.

[0025] Preferably, in step S5, by performing eigenvalue decomposition on the covariance matrix, the major and minor axes of the equivalent ellipse are obtained. The equivalent ellipse has the same first and second moments as the original aggregate. The decomposition formula is as follows:

[0026] Σ=QΛQ -1

[0027] Where Σ is the covariance matrix, Q is the orthogonal matrix composed of eigenvectors, and Λ is the diagonal matrix;

[0028] A two-dimensional polygonal outline can be viewed as a set of points on a plane, i.e., a two-dimensional dataset, represented by points (x, y). i ,y i ) represents, where i = 1, 2, 3...n; the covariance matrix Σ is represented as follows:

[0029]

[0030] Where Cov(x,x) is the variance of variable x, Cov(y,y) is the variance of variable y, and Cov(x,y) = Cov(y,x) is the covariance of x and y.

[0031] In the two-dimensional case, two eigenvalues ​​can be obtained, and the corresponding eigenvectors represent the extension direction of the contour points on the plane. The center of the isoellipse of the aggregate polygon is located at the mean point of the data. The directions of the major and minor axes are determined by the eigenvectors of the covariance matrix. The direction of the major axis corresponds to the eigenvector corresponding to the larger eigenvalue, and the direction of the minor axis corresponds to the eigenvector corresponding to the smaller eigenvalue.

[0032] Preferably, in step S6, Green's formula is used to calculate the area of ​​the polygon;

[0033] Since the polygonal outline of the aggregate can be viewed as a continuous closed curve with a planar region of D and a boundary of C, the area of ​​the polygon can be calculated using Green's formula, as shown below:

[0034]

[0035] Here, P and Q are two functions defined on an open region containing the planar region and its boundary; to more conveniently calculate the area of ​​the polygon, let P = -y / 2 and Q = x / 2. Then, according to Green's theorem, the area of ​​the polygon can be expressed as:

[0036] S=∮ C xdy-ydx.

[0037] Preferably, in step S7, the formulas for calculating the two-dimensional shape index roundness R and the aspect ratio AR of the equivalent ellipse are as follows:

[0038]

[0039] Where p is the perimeter of the two-dimensional projection of the aggregate particle, and A is the area of ​​the two-dimensional projection of the aggregate particle.

[0040]

[0041] Among them, L max L is the length of the major axis of the equivalence ellipse. min It is the length of the minor axis of the equivalence ellipse.

[0042] Preferably, the method can process not only single image information, but also multiple image information at once.

[0043] This invention provides a method for rapidly testing the two-dimensional shape characteristics of aggregates, which has the following advantages compared with existing technologies:

[0044] (1) This invention achieves efficient analysis of aggregate morphological characteristics through an aggregate image measurement system. While accurately measuring basic geometric parameters such as the major and minor axes of the aggregate, digital image information of the aggregate is simultaneously acquired. Through operations such as grayscale conversion, threshold segmentation, image binarization, and image inversion, images usable for information extraction are obtained. Combined with the Canny algorithm and Green's formula, information such as the perimeter and area of ​​the aggregate polygons is calculated, and then morphological characteristic information such as roundness and the aspect ratio of equivalent ellipses is calculated. This invention can quickly and accurately obtain sufficient key indicators of aggregate shape characteristics, and has the advantages of simple operation, high detection efficiency, and high accuracy of results, providing a fast and efficient technical means for quantitatively analyzing the impact of aggregate shape characteristics on the performance of asphalt concrete and cement concrete.

[0045] (2) This invention proposes a systematic and comprehensive method for calculating the two-dimensional shape characteristic parameters of aggregates. This invention uses an image acquisition device to obtain high-precision two-dimensional image information of aggregates. Through image binarization processing, contour detection, and eigenvalue covariance calculation, key information such as the major axis length, minor axis length, angularity, roundness, and aspect ratio of the equivalent ellipse is obtained. Compared with existing methods, this invention innovatively proposes the aspect ratio of the equivalent ellipse as an index to evaluate the shape characteristics of aggregates, and comprehensively characterizes the two-dimensional features of aggregates by integrating multiple indices. This ultimately forms an efficient image processing method for aggregates and a method for calculating two-dimensional shape characteristic indices. This method is more effective, accurate, and sensitive in quantifying and distinguishing aggregate shape characteristics, and features fast processing speed and high accuracy. It is of great significance for efficiently and accurately obtaining the shape characteristics of aggregates and further exploring their impact on the performance of asphalt concrete and cement concrete. Attached image description:

[0046] Figure 1 This is a schematic diagram of the testing process of the present invention;

[0047] Figure 2 This is a scanned image of the aggregate in Embodiment 1 of the present invention;

[0048] Figure 3 This is a diagram of the processed aggregate in Embodiment 1 of the present invention;

[0049] Figure 4 This is a scanned image of the aggregate in Embodiment 2 of the present invention;

[0050] Figure 5 This is a diagram of the aggregate after processing in Embodiment 2 of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1:

[0053] A method for rapidly testing the two-dimensional shape characteristics of aggregates includes the following steps:

[0054] S1. Obtain a sufficient quantity of aggregates within the specified particle size range, and clean, dry, and cool the aggregates to room temperature. In this embodiment, the aggregate particle size is 9.5-13.2mm, and the number of aggregate information collected is 50.

[0055] S2. An aggregate image measurement system (AIMS) is used to acquire aggregate image information, obtaining the major axis length, minor axis length, and image files for this batch of aggregates. The obtained image files are in TIFF format. Each image in the obtained image files contains only one aggregate information, resulting in a total of 50 image files. The obtained aggregate images are as follows: Figure 2 As shown.

[0056] S3. Process the aggregate image, including grayscale conversion, threshold segmentation, image binarization, and image inversion, to obtain the processed aggregate image. (To better obtain aggregate image information, the image is binarized using an algorithm, and polygons are set as foreground elements, i.e., polygon values ​​are 1 and background values ​​are 0). The segmented aggregate image is shown below. Figure 3 As shown.

[0057] S4. Locate the aggregate polygons in the current image and obtain the polygon boundaries. The image is processed using the Canny algorithm. In this embodiment, each aggregate image contains only one aggregate polygon.

[0058] The Canny edge detection algorithm can be used to read multiple aggregate image information from a single image at once. The specific implementation process is as follows:

[0059] Gaussian filtering is used to reduce noise in the image. In Gaussian smoothing, pixels at different locations are assigned different weights, so that neighboring pixels have larger weight values. Gaussian filtering with a kernel size of 3*3 or 5*5 can be used for processing. The kernel size must be N*N and be an odd number greater than 1.

[0060] The Sobel operator is used to calculate the gradient of the image. The convolution template is applied to the horizontal and vertical directions respectively to obtain the calculated gradient value and direction. The gradient direction can be divided into four directions: 0°, 45°, 90° and 135°.

[0061] A non-maximum suppression strategy is employed. Among the pixels that are detected as potential edges, only the pixels with the largest local gradient are retained, while the edge responses corresponding to non-maximum pixels are suppressed to 0. This results in more accurate edges with a single pixel width. When the gradient direction is 45°, the detection formula is:

[0062]

[0063] The edges of the aggregate polygons are then determined based on dual threshold detection, where the high threshold is set to 25% of the peak gradient amplitude and the low threshold is set to 40% of the high threshold, i.e., 10% of the peak gradient amplitude.

[0064] S5. Obtain the centroid of the aggregate polygon, calculate the first and second moment parameters, and obtain the major and minor axes of the isoellipse through covariance:

[0065] By performing eigenvalue decomposition on the covariance matrix, the major and minor axes of the equivalent ellipse are obtained. The equivalent ellipse has the same first and second moments as the original aggregate. The decomposition formula is as follows:

[0066] Σ=QΛQ -1

[0067] Where Σ is the covariance matrix, Q is the orthogonal matrix composed of eigenvectors, and Λ is the diagonal matrix.

[0068] A two-dimensional polygonal outline can be viewed as a set of points on a plane, i.e., a two-dimensional dataset, represented by points (x, y). i ,y i The expression Σ represents the variance matrix Σ, where i = 1, 2, 3, ..., n.

[0069]

[0070] Where Cov(x,x) is the variance of variable x, Cov(y,y) is the variance of variable y, and Cov(x,y) = Cov(y,x) is the covariance of x and y.

[0071] In the two-dimensional case, two eigenvalues ​​can be obtained, and the corresponding eigenvectors represent the extension direction of the contour points on the plane. The center of the isoellipse of the aggregate polygon is located at the mean point of the data. The directions of the major and minor axes are determined by the eigenvectors of the covariance matrix. The direction of the major axis corresponds to the eigenvector corresponding to the larger eigenvalue, and the direction of the minor axis corresponds to the eigenvector corresponding to the smaller eigenvalue.

[0072] S6. Calculate the area, perimeter, and other information of the aggregate polygon:

[0073] Calculate the area of ​​a polygon using Green's formula;

[0074] Since the polygonal outline of the aggregate can be viewed as a continuous closed curve with a planar region of D and a boundary of C, the area of ​​the polygon can be calculated using Green's formula, as shown below:

[0075]

[0076] Here, P and Q are two functions defined on an open region containing the planar region and its boundaries. To more easily calculate the area of ​​a polygon, let P = -y / 2 and Q = x / 2. Then, according to Green's theorem, the area of ​​the polygon can be expressed as:

[0077] S=∮ C xdy-ydx

[0078] S7. Calculate the roundness, aspect ratio, and other information of all test aggregates:

[0079] The formulas for calculating the two-dimensional shape indices roundness R and the aspect ratio AR of an equivalent ellipse are as follows:

[0080]

[0081] Where p is the perimeter of the two-dimensional projection of the aggregate particle, and A is the area of ​​the two-dimensional projection of the aggregate particle.

[0082]

[0083] Among them, L max L is the length of the major axis of the equivalence ellipse. min It is the length of the minor axis of the equivalence ellipse.

[0084] Finally, the shape characteristic information of all aggregates in this batch was obtained. Table 1 shows the shape characteristic results of 20 particles in this batch.

[0085] Table 1

[0086]

[0087] Example 2:

[0088] A method for rapidly testing the two-dimensional shape characteristics of aggregates includes the following steps:

[0089] S1. Obtain a sufficient quantity of aggregates within the specified particle size range, and clean, dry, and cool the aggregates to room temperature. In this embodiment, the aggregate particle size is 1.18-2.36 mm, and the number of aggregate information collected is 100.

[0090] S2. An Artificial Image Measurement System (AIMS) is used to acquire aggregate image information, obtaining the major axis length, minor axis length, and image files of this batch of aggregates. The obtained image files are in TIFF format. In this embodiment, each image file contains 2-5 aggregate information items, resulting in a total of 32 image files. The resulting images containing multiple aggregate information items are shown below. Figure 4 As shown.

[0091] S3. Process the aggregate image, including grayscale conversion, threshold segmentation, image binarization, and image inversion, to obtain the processed aggregate image. The segmented aggregate image is shown below. Figure 5 As shown.

[0092] S4. Locate the aggregate polygons in the current image and obtain the polygon boundaries. The image is processed using the Canny algorithm. In this embodiment, each aggregate image contains information on multiple aggregate polygons, and this method can identify different polygons in the image.

[0093] S5. Obtain the centroid of the aggregate polygon, calculate the first and second moment parameters, and obtain the major and minor axes information of the iso-ellipse through covariance.

[0094] S6. Calculate the area, perimeter, and other information of the aggregate polygon;

[0095] S7. Calculate the roundness, aspect ratio, and other information of all test aggregates.

[0096] Finally, the shape characteristics of all aggregates in this batch were summarized, and Table 2 shows the shape characteristics of 20 particles in this batch.

[0097] Table 2

[0098]

[0099]

[0100] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for rapidly testing the two-dimensional shape characteristics of aggregates, characterized in that, The method includes the following steps: S1. Clean the aggregate, dry it, and then cool it to room temperature; S2. Acquire aggregate image information using an aggregate image measurement system; S3. Process the aggregate image: including grayscale conversion, threshold segmentation, image binarization, and image inversion, to obtain the processed aggregate image; S4. Locate the aggregate polygons in the processed aggregate image and obtain the polygon boundaries; S5. Obtain the centroid of the aggregate polygon, calculate the first and second moment parameters, and obtain the major and minor axis information of the isoellipse through covariance. By performing eigenvalue decomposition on the covariance matrix, the major and minor axes of the equivalent ellipse are obtained. The equivalent ellipse has the same first and second moments as the original aggregate. The decomposition formula is as follows: in, Let covariance matrix be the variance matrix. It is an orthogonal matrix composed of eigenvectors. It is a diagonal matrix; A two-dimensional polygonal outline can be viewed as a set of points on a plane, i.e., a two-dimensional dataset, represented by points. Let be the expression, where i = 1, 2, 3, ..., n; and the covariance matrix. It is expressed as follows: in, It is the variance of variable x. It is the variance of the variable y. = It is the covariance of x and y; In the two-dimensional case, two eigenvalues ​​can be obtained, and the eigenvectors corresponding to the eigenvalues ​​represent the extension direction of the contour points on the plane. The center of the equivalence ellipse of the aggregate polygon is located at the mean point of the data. The directions of the major axis and the minor axis are determined by the eigenvectors of the covariance matrix. The direction of the major axis corresponds to the eigenvector corresponding to the larger eigenvalue, and the direction of the minor axis corresponds to the eigenvector corresponding to the smaller eigenvalue. S6. Calculate the area and perimeter of the aggregate polygon; S7. Calculate the major axis length, minor axis length, angularity, roundness, and aspect ratio of the equivalent ellipse of all test aggregates to obtain comprehensive two-dimensional shape characteristic indicators of the aggregates.

2. The method according to claim 1, characterized in that: The aggregate particle size range used in step S1 is 0.075mm-26.5mm.

3. The method according to claim 1, characterized in that: In step S2, an aggregate image measurement system is used to acquire aggregate image information. After the test, two-dimensional shape index data including the major axis length, minor axis length, and angularity of the aggregate can be directly obtained, and an aggregate image with the file extension .tif can be obtained.

4. The method according to claim 1, characterized in that: In step S3, the image is binarized using an algorithm, and the polygon is set as the foreground, i.e., the polygon value is 1 and the background value is 0.

5. The method according to claim 1, characterized in that, In step S4, the Canny edge detection algorithm is used to read multiple aggregate image information from a single image at once. The specific implementation process is as follows: S4-1. Use Gaussian filtering to reduce noise in the image. In Gaussian smoothing, pixels at different positions are assigned different weights, so that neighboring pixels have larger weight values. Use Gaussian filtering with kernel size of 3*3 or 5*5 for processing. The kernel size must be N*N and be an odd number greater than 1. S4-2. The Sobel operator is used to calculate the gradient of the image. The convolution template is applied to the horizontal and vertical directions respectively to obtain the calculated gradient value and direction. The gradient direction can be divided into four directions: 0°, 45°, 90° and 135°. S4-3. A non-maximum suppression strategy is adopted. Among the pixels that may be edges, only the pixels with the largest local gradient are retained, and the edge responses corresponding to the non-maximum pixels are suppressed to 0, thus obtaining more accurate edges with a single pixel width. When the gradient direction is 45°, the detection formula is: The edges of the aggregate polygons are then determined based on dual threshold detection, where the high threshold is set to 25% of the peak gradient amplitude and the low threshold is set to 40% of the high threshold, i.e., 10% of the peak gradient amplitude.

6. The method according to claim 1, characterized in that: In step S6, Green's formula is used to calculate the area of ​​the polygon; Since the polygonal outline of the aggregate can be viewed as a continuous closed curve with a planar region of D and a boundary of C, the area of ​​the polygon can be calculated using Green's formula, as shown below: Here, P and Q are two functions defined on an open region containing the planar region and its boundary; to more conveniently calculate the area of ​​the polygon, let P = -y / 2 and Q = x / 2. Then, according to Green's theorem, the area of ​​the polygon can be expressed as: 。 7. The method according to claim 1, characterized in that: In step S7, the formulas for calculating the two-dimensional shape index roundness R and the aspect ratio AR of the equivalent ellipse are as follows: Where p is the perimeter of the two-dimensional projection of the aggregate particle, and A is the area of ​​the two-dimensional projection of the aggregate particle. Among them, The length of the major axis of the equivalence ellipse. It is the length of the minor axis of the equivalence ellipse.

8. The method according to any one of claims 1-7, characterized in that: The method can process not only single image information, but also multiple image information at once.

Images