Aggregate particle morphology quantification method and system based on Fourier spectrum decoupling

Abstract

The invention relates to the field of aggregate morphology detection and image processing, and discloses an aggregate particle morphology quantification method and system based on Fourier spectrum decoupling, and the system comprises a three-dimensional point cloud collection module which is used for obtaining the three-dimensional point cloud data of aggregate particles; the point cloud data processing module is used for processing the three-dimensional point cloud data to obtain the two-dimensional contour data; the morphological feature analysis module is used for processing the two-dimensional contour data to obtain aggregate decoupling feature data; and the detection and analysis module is used for associating the modules. According to the method, the RGB-D depth point cloud system is used for collecting multi-view point cloud, and the complete three-dimensional morphology point cloud is obtained through noise reduction and registration. Through Monte Carlo simulation sectioning, two-dimensional equal circumference mapping, contour Fourier series fitting, gradient operator definition and the like, a method for performing decoupling calculation on the shape, the corner angle and the texture of the aggregate through aggregate form parameters is established, and the accuracy and the accuracy of aggregate three-dimensional form test analysis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of aggregate morphology detection and image processing, and in particular to an aggregate particle morphology quantification method and system based on Fourier spectrum decoupling. Background Art

[0002] Aggregate quality testing and evaluation is an important issue in the field of road engineering materials. Aggregate morphology has a decisive influence on the mechanical properties, durability and service behavior of asphalt mixtures. Aggregates account for the vast majority of asphalt pavement materials. Their macroscopic shape (such as cubes or needles), microscopic edges (sharpness) and microscopic texture (surface roughness) together constitute the physical basis of the skeleton embedding effect and interfacial adhesion, directly affecting the key performance of the pavement, such as high-temperature rutting resistance, low-temperature cracking resistance, and water damage resistance.

[0003] Traditional detection technologies such as the vernier caliper method and the gap ratio method can only obtain composite indicators of shape and edges, which are macro-generalized measurements. Two-dimensional visual measurement calculates parameters such as roundness and edges by projecting contours, but the three-dimensional texture fluctuations are compressed, resulting in systematic deviations. The three-dimensional scanning algorithm based on the convex body assumption will produce serious angular misjudgments in concave areas. The CT transmission method is difficult to promote due to its high cost, high threshold and low efficiency. In view of the practical application requirements for scientific, accurate and efficient testing methods, an efficient and scientific analysis and testing method is urgently needed. Summary of the invention

[0004] The purpose of the present invention is to solve the defects existing in the above-mentioned technical status in a targeted manner, to provide a method for decoupling and quantifying aggregate particle morphology based on Fourier spectrum descriptors, to realize the decoupling and quantitative analysis of the common morphological characteristics of concave and convex aggregates, and to provide technical support for the morphological quantification of aggregates and granular materials.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: a method for quantifying aggregate particle morphology based on Fourier spectrum decoupling, comprising the following steps:

[0006] Step 1: Select aggregate particle samples, collect multi-view point clouds of aggregate particles through the RGB-D deep point cloud system, perform noise reduction and registration on the multi-view point clouds to obtain the complete three-dimensional point cloud of aggregate particles and obtain the three-dimensional coordinates ;

[0007] Step 2: Reconstruct the point cloud surface, use Monte Carlo simulation to randomly cut the point cloud to obtain a set of contour surfaces, reduce the dimensions of the points on each cut contour surface, and obtain the coordinates of the two-dimensional contour ;

[0008] Step 3: Map the original contour points of the two-dimensional contour coordinates to isocircle, sample the isocircle to obtain sampling points, and inversely calculate the sampling mapping coordinates on the original contour according to the sampling mapping;

[0009] Step 4: Construct the aggregate profile descriptor Ω based on Fourier series fitting, and solve the aggregate profile descriptor Ω according to the sampling mapping coordinates ;

[0010] Step 5: Calculate the gradient of the aggregate profile descriptor Ω and the gradient direction angle , construct the gradient direction integral, and transform it into a discrete numerical integral to obtain the two-dimensional aggregate morphology parameters ;

[0011] Step 6: Based on the Monte Carlo cutting, isoperimetric circle mapping coordinates and parameters in Steps 2, 3, 4, and 5 , construct a double integral and perform discrete numerical integration to obtain the three-dimensional aggregate morphology parameters ;

[0012] Step 7: According to the Fourier series order threshold, decouple and calculate the aggregate morphology to obtain the three-dimensional aggregate morphology parameters characterizing the aggregate particle morphology characteristics at different scales data.

[0013] Preferably, in Step 1, the selected nominal maximum size range of the sieved aggregate is 2.36 mm to 19 mm.

[0014] Preferably, in Step 1, using a grating scanning system for depth point cloud acquisition has advantages such as high precision and simple operation.

[0015] Preferably, in Step 1, based on the point cloud coordinates obtained by the grating scanning system, the point cloud is denoised, filtered, and smoothed using CloudCompare software and then stitched to obtain the complete scanned point cloud of the aggregate outer surface.

[0016] Preferably, in Step 2, according to the coordinates of the surface point cloud of the aggregate particles, based on Monte Carlo simulation, an external spherical surface is first constructed and a Fibonacci grid point is established, and the cross-section of the contour is obtained through randomized cutting and spatial dimensionality reduction.

[0017] An aggregate particle morphology decoupling and quantification system based on Fourier spectrum descriptors includes: a three-dimensional point cloud acquisition module for obtaining three-dimensional point cloud data of aggregate particles; a point cloud data processing module for processing the three-dimensional point cloud data to obtain the two-dimensional contour data; a morphological feature analysis module for processing the two-dimensional contour data to obtain aggregate decoupling feature data; a detection and analysis module for correlating the modules to realize the functions of aggregate point cloud acquisition, point cloud processing, and morphological analysis, and summarizing and analyzing the data results, and the system operates using the above detection method.

[0018] The present invention provides a method and system for quantifying the morphological characteristics of aggregate particles based on Fourier spectrum decoupling, having the following beneficial effects:

[0019] 1. The present invention uses an RGB-D depth point cloud system to collect multi-viewpoint clouds, and obtains a complete three-dimensional point cloud through noise reduction and registration. Through Monte Carlo simulation slicing, combined with isoperimetric circle mapping, Fourier series fitting, gradient calculation, etc., aggregate morphological parameters are accurately constructed from multiple dimensions of two-dimensional and three-dimensional, improving the test accuracy.

[0020] 2. The method of the present invention can achieve decoupled quantitative analysis of morphological characteristics through systematic steps for both concave coarse aggregates with complex shapes and relatively regular convex coarse aggregates, breaking through the limitations of traditional detection and being applicable to various shaped aggregates.

[0021] 3. The present invention consists of modules such as three-dimensional point cloud acquisition and point cloud data processing. The raster scanning system of the acquisition module has high precision and is easy to operate. The data processing is carried out with the help of professional software, and each module works in coordination, facilitating the rapid acquisition, processing, and analysis of aggregate data on-site. Description of the Drawings

[0022] Figure 1 Schematic diagram of isoperimetric circle mapping of the contour of the present invention;

[0023] Figure 2 Flowchart of the method of the present invention;

[0024] Figure 3 Flowchart of the system of the present invention. Detailed Embodiments

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0026] Please refer to the attached Figure 1 - attached Figure 3 , the embodiments of the present invention provide a method for quantifying the morphological characteristics of aggregate particles based on Fourier spectrum decoupling, including the following steps:

[0027] I. Aggregate sample selection and scanning:

[0028] The selected aggregate samples should be clean and dry. A binocular structured light depth scanning device is used to scan the aggregate particle surface RGB-D depth point cloud data at different viewing angles. The depth point cloud is processed by CloudCompare software in sequence for noise reduction, cropping, splicing, and fine interpolation and smoothing, and then saved as a .pcd format file.

[0029] 2.2D contour acquisition and mapping processing

[0030] According to the surface point cloud scanned in step 1, the implicit surface of the point cloud is reconstructed using Poisson, and the triangular mesh of the point cloud surface is generated based on the Marching Cubes algorithm. , the centroid of the point cloud is calculated using the triangle weighted average method ;

[0031] The radius is the distance between the contour point with the longest distance from the surface point cloud to the centroid and the centroid. , construct a spherical surface with the center of mass as the center of the circle, and calculate the spherical coordinates of the Fibonacci grid points according to the following formula and and converted to three-dimensional coordinates in Cartesian system :

[0032]

[0033] In the formula, is the radius, is the polar angle, is the azimuth, It is the golden ratio;

[0034] Create an index collection for Fibonacci grid points The Fisher-Yates random algorithm is used to shuffle the indexes of the grid points. Construct the centroid of the grid points As a starting point, The direction vector of the end point ;

[0035] Using the formula Determine the direction vector The plane with normal vector Through the point cloud centroid The cutting plane at that time traverses the triangular mesh , for each triangle The distance from each vertex to the cutting plane is calculated according to the following formula :

[0036] In the formula, is the plane normal vector, is the vertex vector of the triangle, is a point on the sectioning plane;

[0037] According to the distance from the vertex of each triangle mesh to the cutting plane , determine any side of the triangle Corresponding vertex-cutting plane distance If the condition is met, then the edge Intersection point on the cutting plane Satisfy the parameters and After the calculation is completed, the points on the contour line after one sectioning can be obtained. ;

[0038] According to the direction vector The plane with normal vector Through the point cloud centroid A set of two-dimensional orthogonal bases are defined on the cutting plane. To construct a local coordinate system, the three-dimensional points on the contour line obtained by cutting The following formula can be used to project to the cutting plane to achieve dimensionality reduction and obtain the two-dimensional coordinates :

[0039] ①Translate to the origin of the local coordinate system:

[0040] ②Calculation On the base Coordinates on , projected onto the cutting plane:

[0041] In the formula, are the coordinates of the three-dimensional points on the contour line obtained by sectioning, is the coordinate of any point on the cutting contour line after dimension reduction, It is constructed on the cutting surface. is the origin of the local coordinate system of the orthogonal basis.

[0042] Select sampling parameters , usually taken , then the number of sampling points ;

[0043] like Figure 1 As shown, the perimeter of the original contour is established Circles of equal circumference have a radius of , with polar angle The starting point on the circle . When sampling, scan the isocircle from the starting point, according to Scan points on the circumference , each sampling point To starting point Arc length can be calculated from the radius of the isoperimetric circle and its polar angle ;

[0044] On the original contour, also take the polar angle as the starting point , calculate the distance of each point on the contour from the starting point along the contour . Taking as the distance marker, scan each point along the contour in turn from the starting point . If , directly record the coordinates of the point , otherwise record the adjacent front and rear points that satisfy and linearly interpolate the coordinate points, denoted as , establish the unique mapping relationship of any point on the contour from the sampling points on the isoperimetric circle, and obtain the mapping coordinates .

[0045] III. Decoupling and Quantitative Calculation of Aggregate Particle Morphology

[0046] Calculate the centroid of the two-dimensional contour , take the centroid as the origin of the Cartesian coordinate system , and take the intersection point of the horizontal axis and the contour as the starting point . The inclination angle of any point with the X-axis is , and the point is then parametrically represented as ;

[0047] Based on the Fourier series and the unique sampling mapping set of the original contour points , construct the aggregate contour descriptor Ω:

[0048] In the formula, and and and are the coefficients of the Fourier series, is the order of the series ;

[0049] The number of contour sampling mapping points is much more than the undetermined Fourier coefficients of the descriptor Ω, which is . Substitute the coordinates , and solve the Fourier coefficients of the descriptor Ω based on the least squares method to obtain the numerical representation of the descriptor Ω:

[0050]

[0051] In the formula, and and and are the coefficients of the Fourier series, For points , is the order of the series , the meanings of other symbols are the same as above.

[0052] According to the derivative of the aggregate profile descriptor Ω, the gradient and gradient direction angle It can be calculated by the following formula:

[0053]

[0054]

[0055]

[0056]

[0057] In the formula and is the derivative of the aggregate profile descriptor Ω;

[0058] Gradient direction angle Polar angle with isoperimetric circle only There is a unique corresponding mapping relationship, that is, there is a unique relationship with the original contour points of the aggregate, which is not constrained by the concavity and convexity of the contour geometry. The degree of contour morphology change can be determined by The integral of the change of is used to obtain the two-dimensional aggregate morphology parameters And its discrete numerical integration formula:

[0059]

[0060]

[0061] In the formula, is the number of points on the contour.

[0062] Three-dimensional aggregate morphology parameters , several sectioning contours of the original particle 3D point cloud can be obtained through the Monte Carlo sectioning process, and each sectioning contour constitutes a point set , and the two-dimensional aggregate morphology parameters of each contour are obtained through steps 3, 4, and 5 Points:

[0063]

[0064]

[0065] In the formula, is the total number of contour surfaces obtained after the three-dimensional point cloud is sectioned by Monte Carlo, is the number of points on the contour, is the integration variable.

[0066] The morphology of the aggregate is divided into three scales: shape, edges and corners, and texture, which are decoupled by the Fourier series order threshold. For the series order of the descriptor Ω, describes the shape feature, describes the edges and corners feature, describes the texture feature;

[0067] The analysis of the three-dimensional shape feature of the aggregate is calculated by the three-dimensional aggregate morphology parameter ; the analysis of the three-dimensional edges and corners feature of the aggregate is calculated by the three-dimensional aggregate morphology parameter ; the analysis of the three-dimensional shape feature of the aggregate is calculated by the three-dimensional aggregate morphology parameter is calculated.

[0068] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An aggregate particle morphology quantification method based on Fourier spectrum decoupling, characterized in that It includes the following steps: S1. Multi-view 3D point cloud acquisition and noise reduction registration: Select aggregate particle samples, collect multi-view point clouds of aggregate particles through an RGB-D depth point cloud acquisition system, perform noise reduction registration on the multi-view point clouds to obtain the complete 3D point cloud of aggregate particles, and obtain 3D coordinates ; S2. Point cloud surface modeling and random sectioning for dimensionality reduction: Reconstruct the point cloud surface, use Monte Carlo simulation to randomly section the point cloud to obtain a set of contour surfaces, and reduce the dimensionality of the points on each section contour surface to obtain the coordinates of the two-dimensional contours. ; S3. Isoperimetric circle mapping and inverse sampling coordinate calculation: Perform isoperimetric circle mapping on the original contour points of the two-dimensional contour coordinates, sample the isoperimetric circle to obtain sampling points, and calculate the sampling mapping coordinates on the original contour according to the sampling mapping. S4. Fourier series contour descriptor modeling and solution: Based on Fourier series fitting, an aggregate contour descriptor Ω is constructed, and the aggregate contour descriptor Ω is solved according to the sampling mapping coordinates Solve the aggregate contour descriptor Ω; S5. Gradient field integration and two-dimensional morphological parameter extraction: Calculate the gradient of the aggregate contour descriptor Ω and the gradient direction angle , construct the gradient direction integral, and convert it into a discrete numerical integral to obtain two-dimensional aggregate morphological parameters ; S6. Double integral discretization and three-dimensional morphological parameter generation: Based on the Monte Carlo cutting, isoperimetric circle mapping coordinates, and parameters in Steps 2, 3, 4, and 5 , construct a double integral and perform discretized numerical integration to obtain three-dimensional aggregate morphological parameters ; S7. Multi-scale Fourier spectrum decoupling and morphological feature classification: According to the Fourier series order threshold, the aggregate morphology is decoupled and calculated to obtain three-dimensional aggregate morphology parameters characterizing the aggregate particle morphology features at different scales data 2. The aggregate particle morphology quantification method based on Fourier spectrum decoupling according to claim 1, wherein The aggregate sample selected in step S1 should have a clean surface and dried moisture. A binocular structured light depth scanning device is used for the aggregate sample. RGB-D depth point cloud data of the aggregate particle surface is scanned from different perspectives by the target point feature method. After denoising, cropping, stitching, and refined interpolation smoothing processing of the depth point cloud in sequence by CloudCompare software, a PCD file is obtained.

3. The aggregate particle morphology quantification method based on Fourier spectrum decoupling according to claim 1, wherein In step S2, implicit surface reconstruction is performed on the collected aggregate surface point cloud to generate a triangular mesh on the point cloud surface, calculate the centroid data, establish a circumscribed sphere through the centroid-contour distance, grid the spherical Fibonacci to establish a Monte Carlo direction vector and a spatial random cutting plane, and perform random cutting and dimension reduction on the aggregate surface point cloud to obtain a set of two-dimensional point cloud cutting profiles.

4. The method for quantifying the aggregate particle morphology based on Fourier spectrum decoupling according to claim 1, wherein In step S3, an isoperimetric circle is established based on the length of the original contour, fixed polar angle sampling is performed on the isoperimetric circle, the polar coordinates of the sampling points are inversely mapped to Cartesian coordinates on the original contour, and the nearest linear interpolation calculation is performed on the non-existent points after mapping to obtain the sampling mapping contour coordinates of the original contour.

5. The aggregate particle morphology quantification method based on Fourier spectrum decoupling according to claim 1, characterized in that In step S4, the contour coordinates are parametrically represented with a moving angle based on the unique sampling mapping relationship to construct an aggregate contour descriptor Ω, and a linear equation system is constructed with the help of the point coordinate set to perform least squares solution on the descriptor Ω.

6. The aggregate particle morphology quantification method based on Fourier spectrum decoupling according to claim 1, characterized in that, In step S5, according to the numerical form of the aggregate profile descriptor Ω, calculate the gradient and the gradient direction angle , construct the gradient direction angle integration element to obtain two-dimensional aggregate morphology parameters , and further perform discrete numerical integration to obtain an easily computable parameter value.

7. The method for quantifying the aggregate particle morphology based on Fourier spectrum decoupling according to claim 1, wherein The three-dimensional aggregate morphology parameters in step S6 are obtained by constructing a double integral and performing discretized numerical integration on the two-dimensional aggregate morphology parameters based on the Monte Carlo cutting and isoperimetric circle mapping coordinates in steps 2, 3, and 4 8. The method for quantifying the aggregate particle morphology based on Fourier spectrum decoupling according to claim 1, wherein In step S7, the morphology of the aggregate is divided into three scales: shape, edges and corners, and texture. Decoupling is carried out by defining the Fourier series order threshold, and further three-dimensional aggregate morphology parameters calculated for different aggregate morphological characteristics are obtained. .

9. The aggregate particle morphology quantification system based on Fourier spectrum decoupling according to any one of claims 1-8, characterized in that, It includes: A three-dimensional point cloud acquisition module for acquiring three-dimensional point cloud data of aggregate particles; A point cloud data processing module for processing the three-dimensional point cloud data to obtain two-dimensional contour data; A morphological feature analysis module for processing the two-dimensional contour data to obtain aggregate decoupling feature data; A detection and analysis module for associating each module to realize the functions of aggregate point cloud acquisition, point cloud processing, and morphological analysis, and summarizing and analyzing the data results. Each of the above modules includes: a three-dimensional point cloud acquisition module, a point cloud data processing module, and a morphological feature analysis module.

10. The aggregate particle morphology quantification system based on Fourier spectrum decoupling according to claim 9, wherein, Run using the analysis and detection method described in any one of claims 2-8.

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