CN109211904B

CN109211904B - Detection system and detection method for two-dimensional internal structure of asphalt mixture

Info

Publication number: CN109211904B
Application number: CN201811064204.6A
Authority: CN
Inventors: 韩森; 孙培; 吴松; 吴晓明; 刘亚敏; 徐鸥明
Original assignee: Changan University
Current assignee: Changan University
Priority date: 2018-09-12
Filing date: 2018-09-12
Publication date: 2021-07-30
Anticipated expiration: 2038-09-12
Also published as: CN109211904A

Abstract

A two-dimensional internal structure detection system and a detection method for an asphalt mixture comprise a file management module, an image preprocessing module, an aggregate counting and screening residue identification module and an internal structure characteristic index calculation module; the file management module, the image preprocessing module, the aggregate counting and screening residue identification module and the internal structure characteristic index calculation module are sequentially connected; the method has the advantages of high testing precision, simplicity in operation, low cost and the like, and can make up for the defect that the traditional method for testing the internal structure of the mixture describes the overall characteristics of the internal structure by using a single evaluation index. The invention can reflect the distribution condition of each component in the asphalt mixture from the aspect of mesoscopic view, can accurately and objectively evaluate the overall characteristics and the distribution characteristics of the internal structure of the asphalt mixture, and is a novel test and evaluation system and method which are low in price and easy to popularize.

Description

Detection system and detection method for two-dimensional internal structure of asphalt mixture

Technical Field

The invention relates to the field of detection of an internal structure of an asphalt mixture, in particular to a system and a method for detecting a two-dimensional internal structure of the asphalt mixture.

Background

The asphalt mixture is a typical multiphase mixture, the distribution of coarse aggregates, asphalt mortar and voids in the mixture directly influences the pavement performance, and the key for researching the asphalt pavement performance is to accurately evaluate the internal structure of the asphalt mixture.

Stereology is a new discipline between morphology and mathematics, and its core idea is to quantitatively represent and describe actual tissues by information obtained from sections (projection views) that are smaller in dimension than the actual tissues, according to a rigorous mathematical approach. Any three-dimensional object can be regarded as being composed of an infinite number of parallel slices, although effective three-dimensional object information cannot be judged by means of a certain single section, the two-dimensional object information contained in the infinite number of slices forms a distribution, and the three-dimensional object information can be calculated according to the distribution. Therefore, the real three-dimensional internal structural characteristics of the mixture can be reflected by using the internal structural characteristic parameters obtained from the two-dimensional section image of the mixture.

With the development of computer processing capability in recent years, the research on asphalt mixture by using digital image processing technology has become a trend. The image analysis of the asphalt mixture at present mainly centers on the characteristics and distribution orientation of each component in the asphalt mixture. In order to quantitatively analyze the internal structure information of the asphalt mixture, it is necessary to first acquire a cross-sectional image of the mixture and then analyze the internal structure of the mixture using a digital image technique. At present, there are two main methods for acquiring a cross-sectional image: the method comprises the following steps that a CCD digital camera and an X-ray CT scanning technology are adopted, wherein the CCD digital camera needs to cut an asphalt mixture test piece so as to obtain a section image, and the X-ray CT scanning technology belongs to a destructive method; the latter utilizes the transmission density of X-ray to obtain the image of the internal structure of the asphalt mixture, belonging to a non-destructive method.

Two-dimensional digital image processing technology based on a CCD camera or a scanner and two-dimensional or three-dimensional digital image processing technology based on X-ray CT both achieve a lot of achievements in the aspect of asphalt mixture microscopic structure analysis. Due to the different principles, the two methods of image acquisition each have advantages and disadvantages. The two-dimensional digital image processing technology based on a CCD camera or a scanner is a destructive test means, a large amount of asphalt mixture test piece sections are needed, the workload of the test piece is large, and the precision of the acquired section image is high. The X-ray CT technology belongs to a nondestructive test means, and utilizes different absorption capacities of materials with different densities to X-rays to obtain a large number of continuous two-dimensional tomographic images, so that the processing is rapid, but the acquired image identification degree is not high due to the reasons that the density of aggregate particles is not uniform, the aggregate particles are wrapped by mucilage and the like.

Compared with the X-ray CT scanning technology, the two-dimensional digital image processing technology based on the CCD camera or the scanner has the advantages of low price, low requirement on equipment and the like, so more and more students analyze the internal microscopic structure of the asphalt mixture by using the method in recent years. However, the processes and methods for studying the internal structure of the asphalt mixture by using the two-dimensional digital image processing technology are not uniform, and the image processing process is greatly influenced by artificial subjective factors. Therefore, it is necessary to find an objective and effective method for evaluating the internal structure of asphalt mixture.

Disclosure of Invention

The invention aims to provide a detection system and a detection method for a two-dimensional internal structure of an asphalt mixture, so as to solve the problems.

In order to achieve the purpose, the invention adopts the following technical scheme:

a two-dimensional internal structure detection system for asphalt mixture comprises a file management module, an image preprocessing module, an aggregate counting and residue screening identification module and an internal structure characteristic index calculation module; the file management module, the image preprocessing module, the aggregate counting and screening residue identification module and the internal structure characteristic index calculation module are sequentially connected; the file management module is used for importing and cutting a section scanning image of the asphalt mixture test piece; the image preprocessing module is used for preprocessing the section scanning image, and performing graying processing, filtering processing, enhancing processing, segmenting processing, aggregate adhesion and hole filling processing on the section scanning image; the aggregate share and residue screening identification module is used for carrying out feasibility verification on the image preprocessing result, and comprises image resolution input, IAP input and laboratory aggregate share and residue screening input; and the internal structure characteristic index calculating module is used for calculating internal structure characteristic indexes including aggregate inclination angle, porosity, mortar ratio, mortar film thickness and uniformity.

Further, the image preprocessing module specifically includes the following operations:

(a) converting the color image into a gray image by adopting a weighted average method for the cut RGB image through an image preprocessing module;

(b) selecting median filtering to carry out smoothing processing on the grayed image, namely carrying out denoising processing on the image;

the mathematical meaning of median filtering can be expressed as formula (1):

f(x,y)＝Med{g(x_i,y_i)}(x_i,y_i)∈A (1)

in the formula, g (x)_i,y_i) Calculating gray values of all pixels in a pixel neighborhood, wherein A is a filtering window;

(c) stretching the gray level within two wave crest ranges of the gray level histogram by adopting linear transformation to increase the contrast ratio of the aggregate and the mortar within the gray level range;

the gray scale range of the input image f (x, y) is [ M, N ], and the gray scale range of the desired transformed image g (x, y) is [ M, N ], then the gray scale linear transformation can be expressed as equation (2):

(d) segmenting the section image of the asphalt mixture test piece by adopting a maximum inter-class variance method;

the total number of pixels of the image is N, the total number of gray levels is L, and the corresponding gray scale range is [0, L-1 ]]The number of pixels having a gray value i is n_iThe mathematical expression of N and the probability expressions of the respective gradation values are expressed by the expressions (3) and (4).

Setting a threshold T divides pixels in an image into two regions: the gray value is in [0, T-1 ]]Region C of inter-pixel structure₀And the gray value is in [ T, L-1 ]]Region C of inter-pixel structure₁. Then region C₀And C₁The probabilities of (d) are respectively:

entire image, region C₀Region C₁Average gray levels mu, mu of₀、μ₁Can be expressed as:

when the difference of the overall gray-scale values in the two divided regions is larger, the difference of the average gray-scale values in the corresponding regions is also larger, and the total variance σ between the regions can be used_B ²To describe its variability;

when sigma is_B ²When the maximum value is taken, the difference between the average gray values representing the two areas is maximum, and the corresponding T is the optimal segmentation threshold (T is more than or equal to 0 and less than or equal to L);

(e) and carrying out aggregate adhesion and hole filling treatment on the image.

Further, the aggregate-counting and residue-screening identification module specifically comprises the following operations:

(a) aggregate particle identification: determining aggregate particle size and identifying aggregate minimum particle size Dmin;

determining aggregate particle size:

the equivalent elliptical short axis 2b with the same area as the aggregate particles is adopted to represent the particle size of the aggregate particles; in the method for determining the equivalent ellipse, the method of the ellipse which has the same area with aggregate particles and the major axis equal to the aggregate major axis can more accurately represent the actual aggregate characteristics, and the aggregate equivalent minor axis is calculated by the following formula:

in the formula, D_j-the equivalent minor axis of the jth aggregate in the image;

A_j-the area of the jth aggregate in the image;

a_j-the principal axis of the jth aggregate in the image;

aggregate minimum identified particle size D_minIdentification:

the scanning precision adopted is 1200pix/inch, and finally 0.6mm is selected as the minimum identification particle size D of the aggregate_min(ii) a In the stage of identifying the residue of aggregate in the asphalt mixture, only the particle size is larger than D_minAnalyzing the aggregate;

(b) aggregate counting and screening residue identification: the method comprises IAP calculation of the proportion of the identifiable aggregate volume to the total aggregate volume in the grading and surplus identification of aggregate grading;

the asphalt mixture consists of asphalt mucilage, aggregate and gaps, and has the following components

V=V_a+V_s+V_v (16)

In the formula, V,V_a、V_s、V_vRespectively the volume of the mixture, the volume of asphalt cement, the volume of aggregate and the volume of a gap; according to the mechanical screening gradation of a laboratory, the quality of each grade of aggregate can be known; meanwhile, as the relative density of each grade of aggregate is known, the volume of each grade of aggregate can be calculated, and the proportion IAP of the identifiable aggregate volume to the total aggregate volume in the grading is represented by the formula (17):

in the formula, V_IParticle size greater than D_minAggregate volume of (a);

V_ithe volume of the oversize aggregate of the i sieve, the study defaults that the size of the sieve opening of the i sieve is smaller than that of the sieve opening of the i +1 sieve;

aggregate counting and screening residue identification:

the total number of pixels in the region where the aggregate particles are located is generally used to represent the area, and the formula is calculated as (18):

A_k=N_k·Δx² (18)

in the formula, A_k-the area of the kth aggregate particle in the image;

N_k-the number of pixel points contained by the kth aggregate particle;

Δ x — the length of an individual pixel point, depending on the image resolution.

After calculating the area of aggregate particles in the image, neglecting that the particle size is smaller than D_minThe aggregate particle area A of the identified aggregate_ICan be calculated by equation (19):

in the formula, D_kThe equivalent minor axis of the kth aggregate in the image.

Aggregate particle total area A_sCan be expressed as:

passing through an i-sieve, the size of the sieve pore is more than or equal to 0.6mm

Can be calculated according to equation (21):

wherein A is_jIs equivalent to a minor axis between [ i, i +1 ]]The jth aggregate area within the sieve pore size interval, and D_i＜D_j＜D_i+1；A_iIs equivalent to a minor axis between [ i, i +1 ]]Aggregate area sum within the sieve pore size interval;

(c) and carrying out feasibility verification on the image preprocessing result, limiting the selection of parameter values in the preprocessing process, and requiring the input parameter values to ensure that the output result meets the following requirements: aggregate fraction PR through screens using digital image recognition_i ^AActual value PR determined by laboratory_iThe error of (2) is kept at [ -5%, 5%]The probability of the error is more than or equal to 95 percent;

(d) and when the difference value of the calculated screen residue of each grade of aggregate is within the judgment standard, carrying out the next operation, otherwise, continuously debugging various template parameters in the image preprocessing process until the judgment standard is met.

Further, the internal structure characteristic index calculation module specifically includes the following operations:

(a) aggregate dip angle study

The main axis of the particles refers to the length between two points with the farthest distance between the boundaries of the particles, and an included angle sigma (-90 degrees to sigma 90 degrees) between the main axis of the particles and the horizontal direction is defined as an aggregate inclination angle for representing the distribution direction of the aggregate in the asphalt mixture;

let the coordinates of the two intersections of the principal axis and the grain boundary be (x)_i,y_i) And (x)_i+1,y_i+1) Major axis d of aggregate particles_maxNumber of aggregate inclination angle sigmaThe mathematical expression may be expressed as:

frequency P distributed by aggregate inclination angle_iAnd aggregate inclination angle average value sigma_mTo describe aggregate distribution status; dividing the inclination angle of aggregate into [ -90 °, -80 ° ]]、[-80°，-70°]……[70°，80°]、[80°，90°]The 18 intervals are equal, and the distribution frequency P of the aggregate inclination angles statistically distributed in the 18 inclination angle intervals_i；

P_i=n_i/N(i＝1，2……，18) (24)

In the formula: n is the total number of coarse aggregates on the section of the test piece, N_iThe number of coarse aggregate particles in a certain inclination angle interval;

(b) porosity study

Utilizing IPP image processing software to mark gaps in the cross-section image of the asphalt mixture test piece, so that the gaps can be conveniently extracted from the cross-section image subsequently; counting the void area in each image after the void is extracted, and defining the ratio of the void area in the image to the whole section image area as the calculated void ratio VV_cCalculating a formula shown in formula (25);

VV_c=A_v/A=A_v/(A_c+A_m+A_v) (25)

wherein A is the total area of the cross-sectional image, A_cIs the area of coarse aggregate particles, A_mArea of asphalt mortar, A_vIs the void area in mm²；

(c) Mortar ratio and mortar film thickness measurement

Area A of coarse aggregate_cThen area A of asphalt mortar_mCan be calculated from equation (26);

A_m=A-A_c-A_v (26)

defining the percentage of the area of the asphalt mortar in the area of the mixture as mortar ratio and mortar ratio

The formula (27) is shown below in units%; definition of mortar film thickness T_mThe ratio of the area of the asphalt mortar to the perimeter of the coarse aggregate particles can be calculated by equation (28) in mm.

T_m=A_m/L_c (28)

In the formula, L_cIs the perimeter, mm, of the coarse aggregate particles in the image;

(d) homogeneity study of the mix

Dividing the section of the test piece into 4 areas with equal area; selecting a dividing method of 'Tian font + Hui font'; taking the center of the section as a central point, dividing the section into a field-shaped region to analyze the distribution state of the aggregate particles in each direction

And (3) counting the area of the coarse aggregate particles in each area, and calculating the variation coefficient of the sum of the areas of the coarse aggregate particles in each area according to the formula (31) and the formula (32) under different division methods.

In the formula, S_jNumbering the sum of the areas of the coarse aggregate particles in the region j, wherein j is 1, 2, 3 and 4;

S_ji-the area of the ith coarse aggregate particle in region j, i ═ 1, 2, …, m;

s-average area of coarse aggregate particles in each zone;

k_t、k_hand under the mode of dividing the Chinese character Tian type and the Chinese character Hui type, the variation coefficient of the sum of the areas of the coarse aggregate particles in each area. Taking the k value as an index for evaluating the uniformity of a certain section of the asphalt mixture, wherein a calculation formula is shown as a formula (33);

k＝αk_t+βk_h (33)

wherein α and β are correlation coefficients, α ═ β ═ 0.5; the closer k is to 0, the better the homogeneity of the mix, and if the aggregates are uniformly distributed in the mix, k is 0.

The uniformity K is used as an index for evaluating the internal uniformity of a certain type of mixture, and a calculation formula is shown in the following formula; the larger K is, the more serious the unevenness degree of the asphalt mixture is; wherein, K-0 is characterized in that the aggregates are uniformly distributed in the asphalt mixture;

in the formula, k_iThe index is the uniformity evaluation index of the asphalt mixture corresponding to the section with the number i, i is 1, 2, …, n; n is the number of sections taken.

Further, a detection method of the asphalt mixture two-dimensional internal structure detection system is based on the asphalt mixture two-dimensional internal structure test system, and comprises the following steps:

step 1, preparing an asphalt mixture test piece;

step 2, acquiring a section scanning image of the asphalt mixture test piece;

step 3, importing a section scanning image, and cutting to generate an RGB image;

step 4, carrying out graying treatment on the section scanning image to obtain a grayscale image, and then carrying out median filtering, image enhancement, threshold segmentation, aggregate adhesion treatment and hole filling treatment;

step 5, verifying the feasibility of the image preprocessing result, including image resolution input, IAP input and laboratory aggregate ingredient and residue screening input;

and 6, calculating internal structural characteristic indexes of aggregate inclination angle, porosity, mortar ratio, mortar film thickness and uniformity.

Further, step 4 specifically includes:

the mathematical meaning of median filtering can be expressed as formula (1):

f(x,y)＝Med{g(x_i,y_i)}(x_i,y_i)∈A (1)

the total number of pixels of the image is N, the total number of gray levels is L, and the corresponding gray scale range is [0, L-1 ]]The number of pixels having a gray value i is n_iMathematical expression of then NAnd the respective gradation value probability expressions are expressed by the expressions (3) and (4).

when the difference of the overall gray-scale values in the two divided regions is larger, the difference of the average gray-scale values in the corresponding regions is also larger, and the total variance σ between the regions can be used_B ²To describeTheir difference;

Further, step 5 specifically includes:

determining aggregate particle size:

A_j-the area of the jth aggregate in the image;

a_j-the principal axis of the jth aggregate in the image;

aggregate minimum identified particle size D_minIdentification:

V=V_a+V_s+V_v (16)

In the formula, V, V_a、V_s、V_vRespectively the volume of the mixture, the volume of asphalt cement, the volume of aggregate and the volume of a gap; according to the mechanical screening gradation of a laboratory, the quality of each grade of aggregate can be known; meanwhile, as the relative density of each grade of aggregate is known, the volume of each grade of aggregate can be calculated, and the proportion IAP of the identifiable aggregate volume to the total aggregate volume in the grading is represented by the formula (17):

in the formula, V_IParticle size greater than D_minAggregate volume of (a);

aggregate counting and screening residue identification:

A_k=N_k·Δx² (18)

in the formula, A_k-the area of the kth aggregate particle in the image;

N_k-the number of pixel points contained by the kth aggregate particle;

in the formula, D_kFor the kth aggregate in the imageEquivalent minor axis.

Aggregate particle total area A_sCan be expressed as:

Can be calculated according to equation (21):

(c) and carrying out feasibility verification on the image preprocessing result, limiting the selection of parameter values in the preprocessing process, and requiring the input parameter values to ensure that the output result meets the following requirements: aggregate-through-sieve screen residue count using digital image recognition

Actual value PR determined by laboratory_iThe error of (2) is kept at [ -5%, 5%]The probability of the error is more than or equal to 95 percent;

Further, step 6 specifically includes:

(a) aggregate dip angle study

let the coordinates of the two intersections of the principal axis and the grain boundary be (x)_i,y_i) And (x)_i+1,y_i+1) Major axis d of aggregate particles_maxThe mathematical expression for aggregate tilt angle σ can be expressed as:

P_i=n_i/N(i＝1，2……，18) (24)

(b) porosity study

VV_c=A_v/A-A_v/(A_c+A_m+A_v) (25)

(c) Mortar ratio and mortar film thickness measurement

A_m=A-A_c-A_v (26)

T_m=A_m/L_c (28)

(d) homogeneity study of the mix

-average area of coarse aggregate particles in each zone;

k＝αk_t+βk_h (33)

Compared with the prior art, the invention has the following technical effects:

the invention discloses a two-dimensional internal structure test evaluation system of an asphalt mixture, which is researched and developed by utilizing an image processing technology, and two-dimensional information contained in a plurality of slices is selected to reflect three-dimensional information of an object based on a stereology principle. And a spatial domain processing method convenient for computer matrix calculation is adopted to carry out a series of preprocessing processes such as graying, median filtering, image enhancement, threshold segmentation, aggregate adhesion and hole filling on the acquired asphalt mixture section image, and a method is provided for verifying the feasibility of the preprocessing process. And then, evaluating the internal structural characteristics of the asphalt mixture by calculating internal structural characteristic indexes of aggregate inclination angle, porosity, mortar ratio, mortar film thickness and uniformity. The method has the advantages of high testing precision, simplicity in operation, low cost and the like, and can make up for the defect that the traditional method for testing the internal structure of the mixture describes the overall characteristics of the internal structure by using a single evaluation index. The invention can reflect the distribution condition of each component in the asphalt mixture from the aspect of mesoscopic view, can accurately and objectively evaluate the overall characteristics and the distribution characteristics of the internal structure of the asphalt mixture, and is a novel test and evaluation system and method which are low in price and easy to popularize.

Drawings

FIG. 1 is a schematic structural diagram of a two-dimensional internal structure test evaluation system for asphalt mixture according to the present invention;

FIG. 2 is a schematic flow chart of the method for testing and evaluating the two-dimensional internal structure of the asphalt mixture according to the present invention;

FIG. 3 is a schematic diagram of a cutting process of the asphalt mixture test piece according to the invention;

FIG. 4 is a cross-sectional image of the asphalt mixture collected in the present invention

FIG. 5 is a gray scale image obtained during image preprocessing according to the present invention;

FIG. 6 is an image obtained after applying median filtering to denoise an image in the image preprocessing process of the present invention;

FIG. 7 is an image obtained after an image is enhanced by piecewise linear transformation in the image preprocessing process according to the present invention;

FIG. 8 is an image obtained by segmenting an image according to the maximum inter-class variance method in the image preprocessing process of the present invention;

FIG. 9 is an image obtained after aggregate adhesion and void filling processes during image preprocessing according to the present invention;

FIGS. 10(a) and (b) are schematic illustrations of aggregate particle inclination angles;

FIG. 11 is a schematic diagram of the void extraction process of the present invention, left panel: an original scanned image; right panel: extracting the images after the gaps;

FIG. 12 is a schematic view of a vertical rectangular cross-section division of a test piece used in the present invention: (a) dividing the shape of the Chinese character Tian; (b) a font style dividing form;

Detailed Description

The invention is further described below with reference to the accompanying drawings:

as shown in fig. 1, the invention provides a two-dimensional internal structure test and evaluation system for asphalt mixture, which comprises:

the file management module is used for importing and cutting a section scanning image of the asphalt mixture test piece;

the image preprocessing module is used for preprocessing the section scanning image, and carrying out graying processing, filtering processing, enhancing processing, segmenting processing, aggregate adhesion, hole filling processing and the like on the section scanning image;

the aggregate share and residue screening identification module is used for carrying out feasibility verification on the image preprocessing result, and comprises image resolution input, IAP input and laboratory aggregate share and residue screening input;

and the internal structure characteristic index calculation module is used for calculating internal structure characteristic indexes including internal structure characteristic indexes such as aggregate inclination angle, porosity, mortar ratio, mortar film thickness and uniformity.

As shown in fig. 2, the invention provides a two-dimensional internal structure test and evaluation system for an asphalt mixture, which specifically comprises the following steps:

step 1, preparing an asphalt mixture test piece;

step 2, acquiring a section scanning image of the asphalt mixture test piece;

step 3, importing a section scanning image and cutting;

step 4, carrying out graying treatment on the section scanning image to obtain a grayscale image, and then carrying out treatments such as median filtering, image enhancement, threshold segmentation, aggregate adhesion treatment, hole filling and the like;

and 6, calculating internal structural characteristic indexes such as aggregate inclination angle, porosity, mortar ratio, mortar film thickness and uniformity.

The technical solution of the present invention is further illustrated by the following tests.

In an exemplary manner, the first and second electrodes are,

(1) test piece for preparing asphalt mixture

Standard marshall test pieces were prepared.

(2) Acquiring a section scanning image of the asphalt mixture test piece

The method adopts the water spray cooling type stone cutting machine to cut the asphalt mixture test piece, for example, for a Marshall test piece, the test piece is cut at the middle position and two sides which are 25.4mm away from the middle position, the cutting mode of the test piece is shown in figure 3, the cutting process is kept slow, and 6 cutting surfaces are formed; and after the cutting is finished, cleaning the surface of the slice, and carrying out air drying in a natural state to carry out the next image acquisition work.

The invention adopts a common flat-panel scanner to scan the section of the asphalt mixture slice, the scanning precision is 1200pix/inch, and the precision meets the test requirements. Errors caused by an external light source need to be noticed in the process of acquiring the asphalt mixture section image by using a scanner, and the acquired image is shown in fig. 4.

(3) Image graying processing

The invention adopts a weighted average method to convert a color image into a gray image. And carrying out gray processing on the image through an image preprocessing module. The image subjected to the image graying processing is shown in fig. 5.

(4) Image denoising process

In the image acquisition process, due to the influence of external factors, such as scratches on a scanner screen, dust, instability of an instrument, and the like, random noise of different degrees appears in the acquired digital image, the image quality is reduced, and even the image characteristics are submerged, so that the image needs to be denoised. The linear smoothing filter inevitably causes blurring while reducing noise, and the median filtering effectively suppresses noise and has much lower blurring effect, so the median filtering is selected by the method for smoothing the asphalt mixture image. The image after the median filtering process is shown in fig. 6.

The mathematical meaning of median filtering can be expressed as formula (1):

f(x,y)＝Med{g(x_i,y_i)}(x_i,y_i)∈A (1)

in the formula, g (x)_i,y_i) To calculate the gray values of all pixels in the pixel neighborhood, a is the filter window.

(5) Image enhancement processing

Considering that the gray level histogram of the asphalt mixture cross-section image has obvious double peaks, the invention finally adopts linear transformation to stretch the gray level in the range of two 'wave crests' of the gray level histogram, thereby increasing the contrast of aggregate and mucilage in the gray level range and facilitating the accurate segmentation of the image. The image after local gray scale interval enhancement is shown in fig. 7.

The essence of the linear point operation is: if the gray scale range of the input image f (x, y) is [ M, N ] and the gray scale range of the desired transformed image g (x, y) is [ M, N ], the gray scale linear transformation can be expressed as equation (2):

(6) image segmentation processing

The purpose of image segmentation is to distinguish between different regions in an image, the principle of which is based on discontinuities or similarities in the gray values of the image. Common threshold segmentation methods include histogram doublet method, iteration method, and maximum inter-class variance method.

Suppose an image has a total number of pixels N, a total number of gray levels L, and a corresponding gray scale range [0, L-1 ]]The number of pixels having a gray value i is n_iThe mathematical expression of N and the probability expressions of the respective gradation values are expressed by the expressions (3) and (4).

when being coveredWhen the difference of the whole gray values in the two divided regions is large, the average gray value difference in the corresponding regions is also large, and the total variance sigma between the regions can be adopted_B ²To describe its differences.

When sigma is_B ²When the maximum value is taken, the average gray value difference of the two areas is maximum, and the corresponding T is the optimal segmentation threshold (T is more than or equal to 0 and less than or equal to L).

Because the maximum inter-class variance method is simple in calculation process, less in time consumption and accurate in calculation result, the maximum inter-class variance method is finally adopted to segment the section image of the asphalt mixture test piece, and the processed image is shown in FIG. 8.

(7) Aggregate adhesion and pore filling treatment

After the mixture image is processed, the adhesion phenomenon still exists among the aggregate particles, and the edge of part of the aggregate is in a sawtooth shape. In addition, a small amount of noise is generated due to uneven gray level distribution of the whole aggregate surface, partial discrete noise points are formed inside the aggregate, and a hole phenomenon exists in the aggregate locally due to the problem of the material of the aggregate. The image is processed by using mathematical morphology, so that the aims of simplifying image data, ensuring the basic shape characteristic of the image and eliminating incoherent structures can be fulfilled.

Erosion and dilation are the most fundamental and important morphological operations that underlie morphological image processing. The small holes in the image and the tiny concave parts at the image boundary can be filled by using the expansion operation, and the tiny burrs in the image can be removed by corrosion. The concept of erosion and expansion is explained below.

To Z²The set of the upper elements A and B, the target A is corroded by the structural element B and is recorded as A theta B, and the expression can be recorded as:

let the structural element B originally located at the origin of the image be in the whole Z²And (4) translating on the plane, and if the B can be completely contained in the A when the origin of the B moves to the z point, the set of all the z points meeting the requirement is the corrosion image of the B to the A. The erosion can be seen essentially as shrinking each subset B + x in image a, congruent with the structuring element B, to x.

Similarly, dilation can be viewed as enlarging each point x in image A to B + x. To Z²Sets A and B of the upper element, the target A being dilated with the structural element B, denoted

Can be written as:

suppose that the initial position of the structuring element B is at the origin of the image, let it be over Z²Moving in a plane, mapping of B with respect to its own origin as it moves to point z

There is a common intersection with A, i.e.

And A, at least 1 overlapping pixel exists, and all z points meeting the requirement are combined into a set which is an expansion image of B to A.

The opening operation and the closing operation are both formed by corrosion and expansion in a composite mode, and the opening operation and the closing operation can be defined according to the corrosion and the expansion. The open operation is to erode the image first and then expand, and the close operation is to expand first and then erode. The structural element B is used to perform an open operation on a, i.e., a is first corroded by B and then expanded by B, which can be expressed as:

the closed operation on a using the structural element B, i.e. a is expanded by B and then eroded by B, can be expressed as:

the invention carries out opening and closing operation on the image after image segmentation by adopting the maximum inter-class variance method, and the image after aggregate adhesion and filling treatment is shown in figure 9.

(8) Aggregate particle identification

(8a) Aggregate size determination

The planform of the aggregate particles is closest to the ellipse, so the study uses the equivalent elliptical minor axis (2b) of the same area as the aggregate particles to characterize the size of the aggregate particle size. In the method for determining the equivalent ellipse, the method of the ellipse which has the same area with the aggregate particles and the major axis which is equal to the aggregate major axis can more accurately represent the actual characteristics of the aggregate, and the aggregate equivalent minor axis can be calculated by the following formula:

A_j-the area of the jth aggregate in the image;

a_j-the major axis of the jth aggregate in the image.

(8b) Aggregate minimum identified particle size D_min：

Limited by the accuracy of the scanned digital image, part of aggregate particles with smaller particle size can not be identified, the scanning accuracy adopted by the invention is 1200pix/inch, and finally 0.6mm is selected as the minimum identification particle size D of the aggregate_min. In the stage of identifying the residue of aggregate in the asphalt mixture, only the particle size is larger than D_minThe aggregate of (a) was analyzed.

(9) Aggregate-score screen residue identification

(9a) IAP calculation:

V=V_a+V_s+V_v (16)

In the formula, V, V_a、V_s、V_vRespectively the volume of the mixture, the volume of the asphalt cement, the volume of the aggregate and the volume of the void. The quality of each grade of aggregate can be known according to the mechanical screening gradation of the laboratory. Meanwhile, since the relative density of each grade of aggregate is known, the volume of each grade of aggregate can be calculated, and the proportion of the identifiable aggregate volume to the total aggregate volume in the gradation (the distribution of the identified aggregate volume in the total aggregate volume, abbreviated as IAP) can be represented by formula (17):

in the formula, V_IParticle size greater than D_minAggregate volume of (a);

V_ithe oversize aggregate volume of the i screen, the study defaults to i screen having a mesh size smaller than that of the i +1 screen (9b) aggregate fraction identification:

A_k=N_k·Δx² (18)

in the formula, A_k-the area of the kth aggregate particle in the image;

N_k-the number of pixel points contained by the kth aggregate particle;

Calculating the area of aggregate particles in the image, neglecting that the particle size (equivalent short axis) is smaller than D_minThe aggregate of (2) can identify the aggregate particle area A_ICan be calculated by equation (19):

in the formula, D_kThe equivalent minor axis of the kth aggregate in the image.

Aggregate particle total area A_sCan be expressed as:

the screen allowance of aggregate is calculated by the I screen (the screen hole size is more than or equal to 0.6mm)

(Percentage of grains substituted on Area size calculated by Area) can be calculated according to formula (21):

wherein A is_jIs equivalent to a minor axis between [ i, i +1 ]]The jth aggregate area within the sieve pore size interval, and D_i＜D_j＜D_i+1；A_iIs equivalent to a minor axis between [ i, i +1 ]]Aggregate area sum within the mesh size interval.

(9c) Verifying the feasibility of image preprocessing:

after the calculated gross screen residue of each grade of aggregate in the image is calculated, the gross screen residue of each grade of aggregate in the mechanical grading of the laboratory can be compared with the gross screen residue of each grade of aggregate in the mechanical grading of the laboratory. Because the subjectivity of the input parameters in the image preprocessing process is high, in order to ensure the accuracy of the image preprocessing process, the selection of parameter values in the preprocessing process needs to be limited, and the input parameter values are required to ensure that the output result meets the following requirements: aggregate-through-sieve screen residue count using digital image recognition

Actual value PR determined by laboratory_iThe error of (2) is kept at [ -5%, 5%]The probability of the reaction is more than or equal to 95 percent.

After the standard is met, the selection of each parameter in the image preprocessing process is judged to be correct, and the internal structure of the mixture can be well identified. If not, the median filtering template and the aggregate adhesion filling processing template need to be continuously adjusted until the requirements are met.

On the basis of meeting the requirements, the internal structural characteristics of the asphalt mixture can be further extracted.

(10) Aggregate dip angle study

The main axis of the particles refers to the length between two points with the farthest distance between the boundaries of the particles, and an included angle sigma (-90 degrees to sigma less than or equal to 90 degrees) between the main axis of the particles and the horizontal direction is defined as an aggregate inclination angle to represent the distribution direction of the aggregate in the asphalt mixture. The aggregate inclination angle can describe the compaction process of the asphalt mixture to a certain extent, and the inclination angle is schematically shown in FIG. 10.

in order to carry out accurate statistical analysis on the overall distribution condition of the aggregate, the distribution frequency P of the aggregate inclination angle is adopted_iAnd aggregate inclination angle average value sigma_m(hereinafter referred to simply as aggregate inclination angle σ)_m) To describe aggregate distribution status. Dividing the inclination angle of aggregate into [ -90 °, -80 ° ]]、[-80°，-70°]……[70°，80°]、[80°，90°]The 18 intervals are equal, and the distribution frequency P of the aggregate inclination angles statistically distributed in the 18 inclination angle intervals_i。

P_i=n_i/N(i＝1，2……，18) (24)

In the formula: n is the total number of coarse aggregates on the section of the test piece, N_iThe number of coarse aggregate particles in a certain inclination angle interval.

In fact, the aggregate particlesThe degree to which the aggregate is "overwhelmed" is the same for inclinations of-90 ° and 90 °, -80 ° and 80 °, etc. Therefore, the aggregate inclination angle sigma is studied_mWhen considering the absolute values of the inclination angles of the aggregates, for example, if there are 4 aggregates in a section, the inclination angles are-70 DEG, -80 DEG, respectively, then the average value of the inclination angles sigma is_mIs (70 ° +70 ° +80 ° +80 °)/4 ═ 75 °.

(11) Porosity study

Firstly, marking gaps in a cross-section Image of an asphalt mixture test piece by using IPP Image processing software (Image Pro-Plus) so as to be convenient for extracting the gaps from the cross-section Image subsequently. The void extraction process is shown in fig. 11, and the white area in the left image of fig. 11 is the void portion. Counting the void area in each image after the void is extracted, and defining the ratio of the void area in the image to the whole section image area as the calculated void ratio VV_cThe formula is calculated as shown in formula (25).

VV_c=A_v/A=A_v/(A_c+A_m+A_v) (25)

Wherein A is the total area of the cross-sectional image, A_cIs the area of coarse aggregate particles, A_mArea of asphalt mortar, A_vIs the void area in mm²。

(12) Mortar ratio and mortar film thickness measurement

From the foregoing, it can be seen that the aggregate particles can be separated by the maximum inter-class variance method, and when the minimum calculated particle size of the aggregate particles is set (the minimum calculated particle size set in this study is 4.75mm), the relevant characteristics of the coarse aggregate particles, including the area a of the coarse aggregate, can be effectively extracted_cThen area A of asphalt mortar_mCan be calculated by equation (26).

A_m=A-A_c-A_v (26)

In order to research the distribution of the asphalt mortar in the asphalt mixture, the percentage of the area of the asphalt mortar to the area of the mixture is defined as the mortar ratio

The formula (27) is shown below in units% of the total weight of the composition. Definition of mortar film thickness T_mThe ratio of the area of the asphalt mortar to the perimeter of the coarse aggregate particles can be calculated by equation (28) in mm.

T_m=A_m/L_c (28)

In the formula, L_cIs the perimeter, mm, of the coarse aggregate particles in the image.

The mortar ratio and the mortar film thickness can both reflect the compactness of the embedding and extruding behavior of the aggregate particles according to the definition of the two formulas. For the same gradation type asphalt mix, the smaller the mortar ratio and mortar film thickness, the more densely the aggregate particles are packed.

(13) Homogeneity study of the mix

If the aggregate particles are uniformly distributed in the asphalt mixture, in any area of the obtained section, the ratio of the area of the coarse aggregate particles to the area of the area is equal to the ratio of the area of all the coarse aggregate particles to the area of the section, namely, the areas of the coarse aggregate particles contained in the two areas with equal areas are equal to each other. When the cross section of the mixture is divided into regions, if the area of each region is divided into too small regions, aggregate particles with larger particle sizes in the regions are easily divided into a plurality of aggregates with smaller particle sizes, so that larger errors are brought, and the reliability of a calculation result is influenced. Considering comprehensively, the section of the test piece is divided into 4 areas with equal area.

The common dividing method mainly comprises concentric circle division, sector division, transverse and longitudinal division, rectangular division and the like, and the dividing method of the Chinese character 'tian' shape and the Chinese character 'hui' shape is finally selected because the section obtained by the method is a rectangular section. Dividing the section into a grid-shaped region by taking the center of the section as a central point so as to analyze the distribution state of the aggregate particles in each direction, wherein the dividing mode is shown in a graph 12 (a); the cross section was divided in a zigzag manner as shown in fig. 12(b) to analyze the distribution state of the aggregate particles at the inside, middle and outside of the cross section.

average area of coarse aggregate particles in each zone;

k_t、k_hand under the mode of dividing the Chinese character Tian type and the Chinese character Hui type, the variation coefficient of the sum of the areas of the coarse aggregate particles in each area.

The larger the coefficient of variation, the more uneven the aggregate distribution. k is a radical of_tThe larger the aggregate distribution is, the more uneven the aggregate distribution in 4 areas divided by the grid shape is; k is a radical of_hThe larger the size, the more uneven the distribution of the aggregate in the 4 regions divided by the reverse font. Under the field-shaped division form, the coefficient of variation mainly reflects the distribution state of the aggregate particles in different angle regions, while the square-shaped form describes the distribution state of the aggregate particles from the radial direction, but the aggregate distribution state under any single division form is difficult to accurately represent the aggregateTrue distribution state of. Therefore, the research integrates the advantages of 2 dividing modes, provides an index for evaluating the uniformity of a certain section of the asphalt mixture by using the k value, and has a calculation formula shown in a formula (33).

k＝αk_t+βk_h (33)

In the formula, α and β are correlation coefficients, and α ═ β ═ 0.5. The closer k is to 0, the better the homogeneity of the mix, and if the aggregates are uniformly distributed in the mix, k is 0.

When the integral uniformity of the asphalt mixture is evaluated, a series of operations such as cutting, scanning, image processing and the like are carried out on a formed test piece, and then the uniformity evaluation index k of the asphalt mixture on a plurality of parallel sections can be obtained_iAnd finally, calculating the integral uniformity of the asphalt mixture according to the uniformity of the plurality of parallel sections.

The uniformity K is used as an index for evaluating the internal uniformity of a certain type of mixture, and a calculation formula is shown in the following formula. The larger K indicates the more serious the unevenness of the asphalt mixture. Among them, K-0 is characterized as an ideal state in which aggregates are uniformly distributed in the asphalt mixture.

The invention discloses an asphalt mixture internal structure testing analysis software 2D-IISAM, which takes SMA-13 as an example, and after entering an operation interface, the specific operation flow is as follows:

(1) a file management module: and clicking the "Open Image", opening the original scanned Image, and clicking the "Crop Image" to cut the Image.

(2) An image preprocessing module: clicking the Gray Processing, and converting the cut RGB image into a Gray image by the system; inputting a median filtering template on the right side of the Med Filt Size button, reading a gray value corresponding to the double peaks according to a displayed gray histogram, inputting an Upper limit value 'Upper' and a Lower limit value 'Lower', and performing local gray enhancement on the image; clicking 'Threshold', automatically identifying a proper Threshold value by the system, and jumping to the next step; inputting a proper adhesion processing template and a hole Filling template, and clicking 'adhesion Size' and 'Filling Size'; finally, clicking the "Label" button, the system labels the aggregate particles.

(3) Aggregate-counting and screen residue identification module: before identifying the aggregate classified and counted screen residue, digital image Precision is required to be input for software to convert the aggregate particle size in the image, the resolution adopted by the research is 1200pix/inch, and therefore 1200 is input in a square on the right side of a tool bar 'Enter Precision'; then inputting IAP value corresponding to each picture, clicking 'Image-based percentage entered', displaying the residue on the middle part of the operation interface (the aggregate particle size is not less than the minimum identification particle size D)_min) (ii) a The calculated residue of each size fraction aggregate is input into a Lab Percent Retained bar frame, and the Grading cut is clicked, so that a laboratory Grading Curve and an image Grading Curve are displayed on the upper part of the cross section. And when the difference value of the calculated screen residue of each grade of aggregate is within the judgment standard, carrying out the next operation, otherwise, continuously debugging each template parameter in the image preprocessing process until the judgment standard is met.

(4) An internal structure characteristic index calculation module: the module comprises two parts of Aggregate calculation and mortar calculation, wherein the minimum calculated particle Size is input to the right side of a Min Aggregate Size toolbar, and 4.75 (unit mm) is input in the research; by sequentially clicking the buttons of "Average organization", "Angle Output", "Homogeneity", "Aggregate Area", "Aggregate period", etc., the software automatically calculates the above-mentioned index and displays it in the right box of the button, wherein "Angle Output" is Output as a file in the format of ". xlsx". The mortar calculation stage is carried out, firstly, the picture after the gap is extracted is opened, a Void Area button is clicked, and the software can output the Area of the gap; according to the result of Aggregate calculation stage, respectively inputting Aggregate Area and Aggregate Perimeter in right side bar frame of 'Enter Aggregate Area' and 'Enter Aggregate Perimeter', clickingThe buttons of 'Mortar Area Ratio' and 'Mortar shock' are used, and the system automatically calculates the Mortar Ratio

Thickness T of mixed mortar film_m。

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims

1. A two-dimensional internal structure detection system for an asphalt mixture is characterized by comprising a file management module, an image preprocessing module, an aggregate counting and screening residue identification module and an internal structure characteristic index calculation module; the file management module, the image preprocessing module, the aggregate counting and screening residue identification module and the internal structure characteristic index calculation module are sequentially connected; the file management module is used for importing and cutting a section scanning image of the asphalt mixture test piece; the image preprocessing module is used for preprocessing the section scanning image, and performing graying processing, filtering processing, enhancing processing, segmenting processing, aggregate adhesion and hole filling processing on the section scanning image; the aggregate share and residue screening identification module is used for carrying out feasibility verification on the image preprocessing result, and comprises image resolution input, IAP input and laboratory aggregate share and residue screening input; the internal structure characteristic index calculation module is used for calculating internal structure characteristic indexes including aggregate inclination angle, porosity, mortar ratio, mortar film thickness and uniformity;

the aggregate-counting and sifting-residue identifying module specifically comprises the following operations:

(a) aggregate particle identification: comprising aggregate size determination and aggregate minimum size D_minIdentifying;

determining aggregate particle size:

using an equivalent elliptical minor axis D of the same area as the aggregate particles_jTo characterize the size of the aggregate particle size; in the method for determining the equivalent ellipse, the method of the ellipse which has the same area with aggregate particles and the major axis equal to the aggregate major axis can more accurately represent the actual aggregate characteristics, and the aggregate equivalent minor axis is calculated by the following formula:

A_j-the area of the jth aggregate in the image;

a_j-the principal axis of the jth aggregate in the image;

aggregate minimum identified particle size D_minIdentification:

V＝V_a+V_s+V_v (16)

In the formula, V, V_a、V_s、V_vRespectively the volume of the mixture, the volume of asphalt cement, the volume of aggregate and the volume of a gap; according to the mechanical screening gradation of a laboratory, the quality of each grade of aggregate can be known; meanwhile, because the relative density of each grade of aggregate is known, the volume of each grade of aggregate is calculated, and the proportion IAP of the identifiable aggregate volume to the total aggregate volume in the gradation is represented by the formula (17):

in the formula, V_IParticle size greater than D_minAggregate volume of (a);

V_ithe volume of the oversize aggregate of the i sieve, the study defaults that the size of the sieve opening of the i sieve is smaller than that of the sieve opening of the i +1 sieve; aggregate counting and screening residue identification:

A_k＝N_k·Δx² (18)

in the formula, A_k-the area of the kth aggregate particle in the image;

N_k-the number of pixel points contained by the kth aggregate particle;

Δ x — the length of a single pixel point, depending on the image resolution;

after calculating the area of aggregate particles in the image, neglecting that the particle size is smaller than D_minThe aggregate particle area A of the identified aggregate_ICalculated by equation (19):

in the formula, D_kIs the equivalent minor axis of the kth aggregate in the image;

aggregate particle total area A_sExpressed as:

Calculated according to equation (21):

wherein A is_jIs equal toEffective minor axis between [ i, i +1 ]]The jth aggregate area within the sieve pore size interval, and D_i＜D_j＜D_i+1；A_iIs equivalent to a minor axis between [ i, i +1 ]]Aggregate area sum within the sieve pore size interval;

2. The asphalt mixture two-dimensional internal structure detection system according to claim 1, wherein the image preprocessing module specifically comprises the following operations:

the mathematical meaning of median filtering is expressed as formula (1):

f(x,y)＝Med{g(x_i,y_i)}(x_i,y_i)∈A (1)

when the gray scale range of the input image f (x, y) is [ M, N ] and the gray scale range of the desired transformed image g (x, y) is [ M, N ], the gray scale linear transformation is expressed by equation (2):

the total number of pixels of the image is N, the total number of gray levels is L, and the corresponding gray scale range is [0, L-1 ]]The number of pixels having a gray value i is n_iThen, the mathematical expression of N and the probability expressions of the respective gray values are expressed by the formula (3) and the formula (4);

setting a threshold T divides pixels in an image into two regions: the gray value is in [0, T-1 ]]Region C of inter-pixel structure₀And the gray value is in [ T, L-1 ]]Region C of inter-pixel structure₁(ii) a Then region C₀And C₁The probabilities of (d) are respectively:

entire image, region C₀Region C₁Average gray levels mu, mu of₀、μ₁Expressed as:

when the difference of the whole gray values in the two divided regions is larger, the average gray value difference in the corresponding regions is also larger, and the total variance sigma between the regions is adopted_B ²To describe its variability;

σ_B ²＝P₀(μ₀-μ)²+P₁(μ₁-μ)²＝P₀P₁(μ₀-μ₁)² (10)

3. The system for detecting the two-dimensional internal structure of the asphalt mixture according to claim 1, wherein the internal structure characteristic index calculation module specifically comprises the following operations:

(a) aggregate dip angle study

The main axis of the particles refers to the length between two points with the farthest distance between the boundaries of the particles, an included angle sigma between the main axis of the particles and the horizontal direction is defined, wherein sigma is more than or equal to-90 degrees and less than or equal to 90 degrees, and the included angle is an aggregate inclination angle and is used for representing the distribution direction of the aggregates in the asphalt mixture;

let the coordinates of the two intersections of the principal axis and the grain boundary be (x)_i,y_i) And (x)_i+1,y_i+1) Major axis d of aggregate particles_maxThe mathematical expression of aggregate inclination angle σ is:

P_i＝n_i/N(i＝1，2……，18) (24)

(b) porosity study

VV_c＝A_v/A＝A_v/(A_c+A_m+A_v) (25)

(c) Mortar ratio and mortar film thickness measurement

Area A of coarse aggregate_cThen area A of asphalt mortar_mCalculated by equation (26);

A_m＝A-A_c-A_v (26)

defining asphalt mortar area to mixThe percentage of the material area is the mortar ratio

The formula (27) is shown below in units%; definition of mortar film thickness T_mIs the ratio of the area of the asphalt mortar to the perimeter of the coarse aggregate particles, calculated by the formula (28), in mm;

T_m＝A_m/L_c (28)

(d) homogeneity study of the mix

Counting the area of the coarse aggregate particles in each area, and calculating the variation coefficient of the sum of the areas of the coarse aggregate particles in each area according to a formula (31) and a formula (32) under different division methods;

-average area of coarse aggregate particles in each zone;

k_t、k_hunder the mode of dividing the Chinese character Tian-shaped aggregate into the Chinese character Tian-shaped aggregate and the Chinese character Hui-shaped aggregate, the sum of the areas of the coarse aggregate particles in each area has a variation coefficient;

taking the k value as an index for evaluating the uniformity of a certain section of the asphalt mixture, wherein a calculation formula is shown as a formula (33);

k＝αk_t+βk_h (33)

wherein α and β are correlation coefficients, α ═ β ═ 0.5; the closer k is to 0, the better the mixture uniformity is represented, and if the aggregates are uniformly distributed in the mixture, k is 0;

4. A detection method of a two-dimensional internal structure detection system for an asphalt mixture is characterized in that the detection system for the two-dimensional internal structure of the asphalt mixture based on claim 1 comprises the following steps:

step 1, preparing an asphalt mixture test piece;

step 2, acquiring a section scanning image of the asphalt mixture test piece;

step 6, calculating internal structural characteristic indexes of aggregate inclination angle, porosity, mortar ratio, mortar film thickness and uniformity;

the step 5 specifically comprises the following steps:

determining aggregate particle size:

A_j-the area of the jth aggregate in the image;

a_j-the principal axis of the jth aggregate in the image;

aggregate minimum identified particle size D_minIdentification:

the scanning precision adopted is 1200pix/inch, and finally 0.6mm is selected as the minimum identification particle size D of the aggregate_min(ii) a In identifying asphalt mixtureThe stage of medium aggregate residue grading, only for the particle size greater than D_minAnalyzing the aggregate;

V＝V_a+V_s+V_v (16)

in the formula, V_IParticle size greater than D_minAggregate volume of (a);

A_k＝N_k·Δx² (18)

in the formula, A_k-the area of the kth aggregate particle in the image;

N_k-the number of pixel points contained by the kth aggregate particle;

Δ x — the length of a single pixel point, depending on the image resolution;

aggregate particle total area A_sExpressed as:

Calculated according to equation (21):

5. The detection method of the asphalt mixture two-dimensional internal structure detection system according to claim 4, wherein the step 4 specifically comprises:

the mathematical meaning of median filtering is expressed as formula (1):

f(x,y)＝Med{g(x_i,y_i)}(x_i,y_i)∈A (1)

σ_B ²＝P₀(μ₀-μ)²+P₁(μ₁-μ)²＝P₀P₁(μ₀-μ₁)² (10)

6. The detection method of the asphalt mixture two-dimensional internal structure detection system according to claim 4, wherein the step 6 specifically comprises:

(a) aggregate dip angle study

The main axis of the particles refers to the length between two points with the farthest distance between the boundaries of the particles, and defines an included angle sigma between the main axis of the particles and the horizontal direction, wherein sigma is more than or equal to 90 degrees and less than or equal to 90 degrees as an aggregate inclination angle for representing the distribution direction of the aggregate in the asphalt mixture;