Asphalt mixture segregation degree nondestructive testing system based on digital image processing

Abstract

The invention belongs to the technical field of asphalt mixture segregation degree detection, and discloses an asphalt mixture segregation degree nondestructive detection system based on digital image processing. Depth segmentation is performed on the aggregate two-dimensional section distribution model so that segregation condition analysis can be performed for different depth layers, segregation areas of all the depth layers can be accurately positioned through the analysis mode, distribution of segregation at different depths of the pavement can be visually presented, and a key basis is provided for judging the pavement quality. The segregation reason can be searched in a targeted manner, a direction is provided for optimizing a construction process, and the quality and the service life of the asphalt pavement are effectively improved. According to the method, the low polarization depth layer is identified based on the polarization degree data, and then pseudo-segregation identification is carried out, so that pseudo-segregation and real segregation can be accurately distinguished through the analysis mode, misjudgment is avoided, and the segregation detection accuracy is improved. The subsequent treatment of the segregation problem is more targeted, unnecessary rectification work is reduced, and the quality of the asphalt pavement is guaranteed.

Description

Technical Field

[0001] The invention belongs to the technical field of asphalt mixture segregation degree detection, and relates to an asphalt mixture segregation degree nondestructive detection system based on digital image processing. Background Art

[0002] Asphalt mixture segregation refers to the uneven distribution of aggregates, asphalt, and air in the mixture due to various factors during the production, transportation, paving, and compaction processes. This uneven distribution can affect pavement strength and durability, reduce pavement smoothness and skid resistance, and shorten pavement service life. Therefore, asphalt mixture segregation analysis based on digital image processing plays an important role.

[0003] Traditional technical solutions lack specific analysis of the segregation conditions at different depths when conducting asphalt mixture segregation degree detection and analysis. This analysis method cannot accurately assess the degree of asphalt mixture segregation, making it difficult to determine the root cause of segregation and take targeted measures, increasing subsequent maintenance costs and safety risks. It is also not conducive to the refined management of construction quality, and has a serious adverse impact on the construction, use and maintenance of road projects.

[0004] Traditional technical solutions for asphalt mixture segregation detection and analysis often lack the ability to analyze false segregation. This approach can easily lead to misjudgment of segregation, resulting in unnecessary rectification and increased costs. It can also miss true segregation issues, causing pavement with quality risks to be put into service, impacting its strength and durability, shortening its service life, and ultimately increasing road maintenance costs and safety risks. Summary of the Invention

[0005] In view of this, in order to solve the problems raised in the above background technology, a non-destructive detection system for the segregation degree of asphalt mixture based on digital image processing is proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions: a non-destructive detection system for the degree of segregation of asphalt mixture based on digital image processing, including: a data acquisition module, which uses a multi-angle image acquisition device to obtain a cross-sectional image of an asphalt mixture sample, performs aggregate type identification and aggregate area segmentation and positioning and constructs a two-dimensional cross-sectional distribution model of aggregate, and uses a polarized light imaging component to obtain polarization degree data of aggregate.

[0007] The data analysis module performs depth segmentation on the two-dimensional aggregate cross-sectional distribution model, obtains aggregate distribution data of multiple depth layers, and calculates the aggregate distribution ratio and aggregate aggregation degree of each depth layer.

[0008] The anomaly identification module identifies the depth layer of abnormal aggregate distribution according to the aggregate distribution ratio and aggregate aggregation, and identifies the low polarization depth layer based on the polarization degree data, matches the two and further identifies whether each depth layer is pseudo-segregation.

[0009] The cause analysis module identifies the cause of segregation for the abnormal depth layer of aggregate distribution that is determined not to be pseudo-segregation. The segregation causes include uneven mixing and crushing by rolling.

[0010] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention performs depth segmentation on the two-dimensional aggregate cross-sectional distribution model to analyze segregation at different depths. This analysis method can accurately locate the segregation area at each depth layer and intuitively present the distribution of segregation at different depths of the pavement, providing a key basis for judging the quality of the pavement. It can also specifically identify the cause of segregation, provide direction for optimizing the construction process, and effectively improve the quality and service life of asphalt pavement.

[0011] (2) The present invention identifies pseudo-segregation by identifying low-polarization depth layers based on polarization data. This analysis method can accurately distinguish pseudo-segregation from true segregation, avoid misjudgments, and improve segregation detection accuracy. This makes subsequent treatment of segregation issues more targeted, reduces unnecessary rectification work, and lowers costs. At the same time, it ensures that areas with true segregation problems are properly addressed, thereby protecting the quality of asphalt pavement. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 This is a schematic diagram of the connection of various modules of the system of the present invention.

[0014] Figure 2 A schematic diagram of the aggregate area corresponding to an embodiment provided by the present invention.

[0015] Figure 3 A schematic diagram of the distance between a monitoring location point corresponding to an embodiment provided by the present invention and other monitoring location points.

[0016] Reference numerals: 1—aggregate area, 2—monitoring location point, 3—distance between the monitoring location point and other monitoring location points. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0018] See also Figure 1 As shown, the present invention provides a nondestructive detection system for the segregation degree of asphalt mixture based on digital image processing, including a data acquisition module, a data analysis module, an abnormality identification module and a cause analysis module, wherein the data acquisition module is connected to the data analysis module, the data analysis module is connected to the abnormality identification module, and the abnormality identification module is connected to the cause analysis module.

[0019] The data acquisition module is used to obtain cross-sectional images of asphalt mixture samples using a multi-angle image acquisition device, perform aggregate type identification and aggregate area segmentation and positioning, and construct a two-dimensional aggregate cross-sectional distribution model, and obtain aggregate polarization degree data using a polarized light imaging component.

[0020] In a preferred embodiment of the present invention, the specific method for aggregate type identification and aggregate area segmentation and positioning is as follows: the cross-sectional image of the asphalt mixture sample obtained by the multi-angle image acquisition device is grayscaled to obtain the grayscale value of each pixel point of the cross-sectional image, and then a similarity analysis is performed with the pre-saved aggregate grayscale value to obtain the grayscale similarity of each pixel point.

[0021] It should be noted that the pre-stored aggregate grayscale values are obtained by analyzing a large number of known aggregate samples. A similarity analysis is performed on the grayscale values of each pixel in the cross-sectional image with the pre-stored aggregate grayscale values. This involves comparing the grayscale values of each pixel to the known aggregate grayscale values. The result of this calculation is the grayscale similarity of each pixel. The higher the grayscale similarity, the greater the likelihood that the pixel belongs to aggregate; conversely, the lower the likelihood. This method can initially screen out pixels that may belong to aggregate, laying the foundation for subsequent accurate identification of aggregate areas.

[0022] Preferably, the grayscale similarity is specifically calculated as follows: performing difference calculation on the grayscale value of each pixel and the grayscale value of the aggregate, taking the absolute value, and then performing ratio calculation on the grayscale value of the aggregate to obtain the grayscale similarity.

[0023] The grayscale similarity is compared with a preset grayscale similarity threshold, and pixels with grayscale values greater than the corresponding threshold are identified as aggregate identification points.

[0024] It's important to note that the preset grayscale similarity threshold is based on extensive experimental data and experience. Researchers analyzed aggregates in various asphalt mixture samples and, based on the distribution of grayscale similarity, established a reasonable threshold. This threshold distinguishes pixels with a high probability of belonging to aggregate from other pixels, ensuring stable aggregate identification across different samples.

[0025] Locate the 3D coordinates of each aggregate identification point, and classify adjacent aggregate identification points to obtain several aggregate identification point sets. Figure 2 As shown, the aggregate identification point set constitutes a number of aggregate areas 1.

[0026] It's important to note that each aggregate identification point set represents a spatially connected group of aggregate identification points, which collectively form several aggregate regions. These aggregate regions visually demonstrate the distribution range and shape of different aggregates within the asphalt mixture sample. Subsequent analysis, such as area calculation, shape analysis, and distribution statistics, will provide a deeper understanding of the aggregate distribution within the asphalt mixture, providing key data support for assessing the degree of segregation within the asphalt mixture.

[0027] In a preferred embodiment of the present invention, the specific method of obtaining the polarization degree data of the aggregate using the polarization imaging component is as follows: the polarization imaging component is composed of a linear polarizer array and a rotary stepping motor.

[0028] The center point of each aggregate area is used as the monitoring position point of each aggregate area, and then a polarization imaging component is used to perform multiple polarization tests based on a pre-set polarization monitoring distance and several polarization angles to obtain the measured polarization degree at different polarization angles.

[0029] It's important to explain that the center of each aggregate area was chosen as the monitoring location because it best represents the average properties of the entire aggregate area. By identifying these locations, subsequent polarization data acquired is more representative and accurate, avoiding data bias caused by selecting edges or other unique locations. For example, in an irregularly shaped aggregate area, the center point can comprehensively reflect the overall optical properties of the aggregate in that area, making data acquired from this point more reliable.

[0030] The measured polarization degrees at different polarization angles are averaged and then the deviation is calculated from the pre-saved new aggregate reference polarization degrees to obtain the polarization degree difference values of each aggregate surface.

[0031] It should be noted that the polarization imaging assembly, consisting of a linear polarizer array and a rotary stepper motor, is used to conduct tests at multiple polarization angles. The rotary stepper motor drives the linear polarizer array to rotate to different pre-set polarization angles, such as 0°, 45°, 90°, and 135°. At each angle, the linear polarizer array polarizes the light reflected or transmitted from the aggregate area. The light signal intensity at each polarization angle is then acquired through the relevant light detection equipment, and the measured degree of polarization is calculated. Multiple polarization tests can obtain polarization information of the aggregate from multiple angles, more comprehensively reflecting the optical properties of the aggregate and providing rich data support for subsequent analysis.

[0032] The data analysis module is used to perform depth segmentation on the aggregate two-dimensional cross-sectional distribution model, obtain aggregate distribution data of multiple depth layers, and calculate the aggregate distribution proportion and aggregate aggregation degree of each depth layer.

[0033] In a preferred embodiment of the present invention, the specific method of depth segmentation of the two-dimensional aggregate cross-sectional distribution model is as follows: obtain the width value of the asphalt mixture sample, and then calculate the ratio with the preset appropriate depth layer width, and round the result of the ratio calculation to obtain the number of depth layer segmentation levels.

[0034] It should be noted that the asphalt mixture sample used in the present invention is a cube, and the specific analysis process is carried out on four sides.

[0035] Exemplarily, the rounding may be rounding up.

[0036] The width of the asphalt mixture sample is calculated by ratioing the width value of the asphalt mixture sample to the number of depth layer segmentation levels to obtain a single depth layer segmentation width. Based on the single depth layer segmentation width, each section of the asphalt mixture sample is vertically cut to obtain a plurality of depth layers.

[0037] In a preferred embodiment of the present invention, the specific calculation method of the aggregate distribution ratio is as follows: each cross-sectional image corresponding to each depth layer is obtained, and then the aggregate area is located.

[0038] The areas of the aggregate regions corresponding to the depth layers are accumulated and calculated to obtain the total area of the corresponding aggregate regions. At the same time, the total areas of the cross-sectional images corresponding to the depth layers are summed and calculated to obtain the total area of the cross-sectional images of the depth layers. The total area of the aggregate regions and the total area of the cross-sectional images are divided into a proportion to obtain the aggregate distribution proportion of the depth layers.

[0039] It should be noted that the aggregate distribution ratio is a key indicator for measuring the distribution of aggregates in different depth layers of asphalt mixtures, and is used to evaluate the degree of segregation of asphalt mixtures. In an ideal uniform asphalt mixture, the aggregate distribution ratio of each depth layer should be roughly the same. If the aggregate distribution ratios of different depth layers are significantly different, for example, the aggregate distribution ratio of a certain depth layer is much higher or lower than that of other depth layers, it indicates that segregation exists in the asphalt mixture. The more depth layers with abnormal aggregate distribution ratios, the more severe the degree of segregation may be. This indicator provides a quantitative basis for engineering personnel to understand the uniformity of aggregate distribution within the asphalt mixture, helps to promptly identify problems during construction and take corresponding measures to improve them, thereby ensuring the quality and performance of asphalt pavement.

[0040] In a preferred embodiment of the present invention, the specific calculation method of the aggregate aggregation degree is as follows: obtain the monitoring position point 2 corresponding to each aggregate area of each cross-sectional image, and then obtain the distance 3 between each monitoring position point and other monitoring position points, compare the distances and select a preset number of monitoring position points with the closest distances as the reference monitoring position point set for aggregation analysis of the monitoring position point.

[0041] It should be noted that aggregate aggregation is an important parameter for evaluating the degree of segregation in asphalt mixtures. It reflects the density of aggregate distribution in asphalt mixtures and can assist in determining the uniformity of asphalt mixtures. The larger the aggregate aggregation value, the more concentrated the aggregate distribution and the more obvious the aggregation phenomenon; the smaller the value, the relatively uniform and dispersed the aggregate distribution. In a uniform asphalt mixture, the aggregate aggregation should be within a reasonable range and the differences between different depth layers should be small. If the aggregate aggregation of a certain depth layer deviates from the normal range, being too high means that the aggregate in that layer is clumped, which will affect the grading and overall performance of the mixture; being too low means that the aggregate distribution is too loose, resulting in insufficient strength and stability of the mixture.

[0042] The distance between each monitoring position point and each monitoring position point in the corresponding reference monitoring position point set is averaged and used as the aggregate distribution spacing of each monitoring position point. The aggregate distribution spacing is ratioed to the preset appropriate distribution spacing and the inverse is taken to obtain the aggregate aggregation degree of each monitoring position point. Then, the aggregate aggregation degree of each depth layer corresponding to each monitoring position point is averaged and the aggregate aggregation degree of each depth layer is obtained.

[0043] It should be noted that the present invention analyzes segregation at different depths by segmenting the two-dimensional aggregate cross-sectional distribution model. This analysis method accurately locates segregation areas at each depth, visually presenting the distribution of segregation at different depths in the pavement, providing a key basis for assessing pavement quality. This allows for targeted identification of segregation causes, providing guidance for optimizing construction processes, and effectively improving the quality and service life of asphalt pavements.

[0044] The anomaly identification module is used to identify the depth layer of abnormal aggregate distribution according to the aggregate distribution ratio and aggregate aggregation, and to identify the low polarization depth layer based on the polarization degree data, match the two and further identify whether each depth layer is pseudo-segregation.

[0045] In a preferred embodiment of the present invention, the specific method of identifying the depth layer with abnormal aggregate distribution is as follows: the aggregate distribution ratio and aggregate aggregation of each depth layer are compared with the corresponding depth layer benchmark range, and the depth layer whose aggregate distribution ratio or aggregate aggregation exceeds the corresponding depth layer benchmark range is recorded as the depth layer with abnormal aggregate distribution.

[0046] The same depth layer benchmark range is obtained based on historical samples through confidence interval analysis.

[0047] In a preferred embodiment of the present invention, the specific method of identifying the low polarization depth layer is as follows: the polarization degree difference value of each aggregate surface in each depth layer is compared with the preset polarization degree difference value threshold, and the aggregate area whose polarization degree difference value of the aggregate surface is greater than the corresponding polarization degree difference value threshold is recorded as a low polarization degree aggregate area.

[0048] The number of low-polarization aggregate areas and the total number of aggregate areas in each depth layer are counted, and then the proportion calculation is performed to obtain the proportion of the number of low-polarization areas in each depth layer. The depth layer whose proportion of the number of low-polarization areas is greater than the preset low-polarization area proportion threshold is recorded as a low-polarization depth layer.

[0049] In a preferred embodiment of the present invention, the specific steps of identifying whether each depth layer is a pseudo-segregation are as follows: matching the depth layer with abnormal aggregate distribution with the low polarization depth layer, and recording the successfully matched depth layer as a suspected pseudo-segregation depth layer.

[0050] It's important to note that pseudo-segregation often exhibits certain characteristics in both aggregate distribution and polarization properties. If a depth layer exhibits both abnormal aggregate distribution and low polarization, it's highly likely to be a pseudo-segregation depth layer. This matching can narrow the scope requiring further analysis and improve detection efficiency.

[0051] It should be noted that true segregation can cause changes in aggregate distribution, causing the aggregate distribution ratio or concentration to exceed the baseline range. However, in practice, other factors such as aggregate aging or contamination may cause the appearance of segregation, which is not a true aggregate distribution problem. Therefore, when a depth layer exhibits both characteristics, it is more consistent with the characteristics of pseudo-segregation and is more likely to be a pseudo-segregation depth layer.

[0052] The aggregate distribution ratio and aggregate aggregation of each suspected pseudo-segregation depth layer are compared with the corresponding depth layer benchmark range respectively. If in a suspected pseudo-segregation depth layer, only one of the aggregate distribution ratio or aggregate aggregation exceeds the corresponding benchmark range, the suspected pseudo-segregation depth layer is identified as a pseudo-segregation depth layer.

[0053] It should be noted that the corresponding depth layer benchmark range is a reasonable data range derived from analyzing a large number of normal asphalt mixture samples using statistical methods such as confidence interval analysis. It represents the fluctuation range of aggregate distribution percentage and aggregate aggregation at each depth layer under normal conditions. In the case of true segregation, the distribution of aggregates will change significantly, and typically both the aggregate distribution percentage and aggregate aggregation will deviate from the normal range. Because segregation is caused by various factors during the construction process that lead to uneven distribution of aggregates in the asphalt mixture, this unevenness will comprehensively affect the distribution characteristics of the aggregates, so both indicators are often abnormal at the same time.

[0054] It should be noted that the present invention identifies pseudo-segregation by identifying low-polarization depth layers based on polarization data. This analysis method can accurately distinguish between pseudo-segregation and true segregation, avoid misjudgments, and improve segregation detection accuracy. This makes subsequent treatment of segregation issues more targeted, reduces unnecessary rectification work and costs, and ensures that areas with true segregation problems are properly addressed, thus safeguarding the quality of asphalt pavement.

[0055] The cause analysis module is used to identify the cause of segregation for the abnormal aggregate distribution depth layer that is determined not to be pseudo-segregation, and the segregation causes include uneven mixing and crushing by rolling.

[0056] In a preferred embodiment of the present invention, the specific method of identifying the cause of segregation is as follows: obtaining the contour of each aggregate region, and then using image processing software to obtain the contour perimeter and contour area of each aggregate region.

[0057] Using the formula DF i =2×(lnL ij / lnA ij ) analysis to obtain the aggregate fractal dimension DF corresponding to each depth layer i , where i represents the depth layer number, i=1,2...I, I represents the number of depth layers, j represents the aggregate area number, j=1,2...J, J represents the number of aggregate areas, L ij A represents the perimeter of the jth aggregate area at the i-th depth layer, ij Represents the contour area of the jth aggregate region at the i-th depth layer.

[0058] It should be noted that fractal dimension is a mathematical concept used to describe the complexity and irregularity of an object. The fractal dimension of aggregate is calculated using a specific formula and is used to quantify the complexity of aggregate shape. The smaller the fractal dimension, the more regular and simple the aggregate shape; the larger the fractal dimension, the more complex and irregular the aggregate shape. Under normal mixing conditions and without abnormal external forces, the aggregate shape is relatively stable, and the fractal dimension remains within a certain range.

[0059] The aggregate fractal dimension corresponding to the depth layer of abnormal aggregate distribution that is judged not to be pseudo-segregation is compared with the preset aggregate fractal dimension threshold. If the aggregate fractal dimension of a depth layer of abnormal aggregate distribution is less than or equal to the corresponding aggregate fractal dimension threshold, the cause of segregation of the depth layer of abnormal aggregate distribution is identified as uneven mixing. If the aggregate fractal dimension of a depth layer of abnormal aggregate distribution is greater than or equal to the corresponding aggregate fractal dimension threshold, the cause of segregation of the depth layer of abnormal aggregate distribution is identified as rolling crushing.

[0060] It needs to be explained that when the cause of segregation is uneven mixing, the distribution of aggregates in the asphalt mixture is uneven, but the aggregates themselves are not severely crushed or deformed. In other words, the shape of the aggregates has not changed significantly and still maintains a relatively regular shape. In this case, the calculated aggregate fractal dimension is relatively small. Therefore, when the aggregate fractal dimension of a depth layer with abnormal aggregate distribution is less than or equal to the preset aggregate fractal dimension threshold, it can be inferred that the cause of segregation in this depth layer is likely to be uneven mixing. For example, during the mixing process, if the mixing time is insufficient or the mixing force is insufficient, it may cause the aggregates in some areas to fail to disperse evenly, but the shape of the aggregates themselves is not significantly affected, which is reflected in the fractal dimension as a low value.

[0061] It should be explained that if segregation is caused by rolling crushing, during the construction rolling process, the aggregate is subjected to greater external forces and broken into smaller and more irregularly shaped particles. These crushed aggregates have more complex contours and shapes, which increases the calculated aggregate fractal dimension. Therefore, when the aggregate fractal dimension of a depth layer with abnormal aggregate distribution is greater than or equal to the corresponding aggregate fractal dimension threshold, it can be judged that the cause of segregation in this depth layer is most likely rolling crushing. For example, during the rolling process of a road roller, if the rolling parameters of the road roller are unreasonable and excessive pressure is applied to the asphalt mixture, the aggregate may be crushed, resulting in an increase in the fractal dimension.

[0062] It's important to note that the preset aggregate fractal dimension threshold serves as a key reference for determining the cause of segregation. This threshold was determined by analyzing a large number of asphalt mixture samples with known segregation causes (e.g., uneven mixing or crushing during rolling), analyzing the distribution range of aggregate fractal dimensions under different conditions, and combining this with practical engineering experience. By comparing the measured aggregate fractal dimension with this threshold, the cause of segregation can be quickly and accurately identified, providing a basis for subsequent targeted improvement measures.

[0063] It should be noted that the purpose of segregation cause identification is to accurately determine the specific cause of segregation in asphalt mixtures, thereby providing a basis for targeted improvement measures. By comparing the aggregate fractal dimension corresponding to the depth layer of abnormal aggregate distribution that is determined not to be pseudo-segregation with the preset aggregate fractal dimension threshold, it can be determined whether the cause of segregation is uneven mixing or crushing during rolling. This allows engineers to adjust the construction process based on the specific cause, such as optimizing mixing time and force to solve uneven mixing problems or reasonably adjusting the roller's compaction parameters to avoid crushing. This improves the quality and uniformity of the asphalt mixture and ensures the quality and performance of the asphalt pavement.

[0064] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. The non-destructive detection system for asphalt mixture segregation degree based on digital image processing is characterized by: include: The data acquisition module uses a multi-angle image acquisition device to obtain cross-sectional images of asphalt mixture samples, identifies aggregate types and segments and locates aggregate regions, constructs a two-dimensional cross-sectional distribution model of aggregates, and uses a polarized light imaging component to obtain aggregate polarization data. The data analysis module performs depth segmentation on the two-dimensional aggregate cross-sectional distribution model, obtains aggregate distribution data of multiple depth layers, and calculates the aggregate distribution ratio and aggregate aggregation degree of each depth layer; An anomaly identification module identifies depth layers with abnormal aggregate distribution based on the aggregate distribution ratio and aggregate aggregation, and identifies low polarization depth layers based on polarization degree data, matches the two, and further identifies whether each depth layer is a pseudo-segregation; The cause analysis module identifies the cause of segregation for the abnormal depth layer of aggregate distribution that is determined not to be pseudo-segregation. The segregation causes include uneven mixing and crushing by rolling.

2. The nondestructive testing system for asphalt mixture segregation degree based on digital image processing according to claim 1, characterized in that: The specific method of performing aggregate type identification and aggregate area segmentation and positioning is as follows: Grayscale processing is performed on the cross-sectional image of the asphalt mixture sample acquired by the multi-angle image acquisition device to obtain the grayscale value of each pixel of the cross-sectional image, and then similarity analysis is performed with the pre-stored aggregate grayscale value to obtain the grayscale similarity of each pixel; Comparing the grayscale similarity with a preset grayscale similarity threshold, identifying pixels with grayscale values greater than the corresponding threshold as aggregate identification points; The three-dimensional coordinates of each aggregate identification point are located, and adjacent aggregate identification points are classified to obtain a plurality of aggregate identification point sets, wherein the aggregate identification point sets constitute a plurality of aggregate regions.

3. The nondestructive testing system for asphalt mixture segregation degree based on digital image processing according to claim 1, characterized in that: The specific method of obtaining the polarization degree data of the aggregate using the polarized light imaging component is as follows: The polarized light imaging assembly is composed of a linear polarizer array and a rotary stepping motor; The center point of each aggregate area is used as the monitoring point of each aggregate area, and then a polarization imaging component is used to perform multiple polarization tests based on a pre-set polarization monitoring distance and several polarization angles to obtain the measured polarization degree at different polarization angles; The measured polarization degrees at different polarization angles are averaged and then the deviation is calculated from the pre-saved new aggregate reference polarization degrees to obtain the polarization degree difference values of each aggregate surface.

4. The nondestructive testing system for asphalt mixture segregation degree based on digital image processing according to claim 1, characterized in that: The specific method of performing depth segmentation on the aggregate two-dimensional cross-sectional distribution model is as follows: Obtain the width value of the asphalt mixture sample, and then calculate the ratio with the preset appropriate depth layer width, and round the result of the ratio calculation to obtain the number of depth layer segmentation levels; The width of the asphalt mixture sample is calculated by ratioing the width value of the asphalt mixture sample to the number of depth layer segmentation levels to obtain a single depth layer segmentation width. Based on the single depth layer segmentation width, each section of the asphalt mixture sample is vertically cut to obtain a plurality of depth layers.

5. The nondestructive testing system for asphalt mixture segregation degree based on digital image processing according to claim 3 is characterized in that: The specific calculation method of the aggregate distribution ratio is as follows: Obtain cross-sectional images corresponding to each depth layer to locate the aggregate area; The areas of the aggregate regions corresponding to the depth layers are accumulated and calculated to obtain the total area of the corresponding aggregate regions. At the same time, the total areas of the cross-sectional images corresponding to the depth layers are summed and calculated to obtain the total area of the cross-sectional images of the depth layers. The total area of the aggregate regions and the total area of the cross-sectional images are divided into a proportion to obtain the aggregate distribution proportion of the depth layers.

6. The nondestructive testing system for asphalt mixture segregation degree based on digital image processing according to claim 5, characterized in that: The specific calculation method of the aggregate aggregation degree is as follows: Obtaining monitoring location points corresponding to each aggregate area in each cross-sectional image, and then obtaining the distance between each monitoring location point and other monitoring location points, comparing the distances and selecting a preset number of monitoring location points with the closest distances as a reference monitoring location point set for aggregation analysis of the monitoring location points; The distance between each monitoring position point and each monitoring position point in the corresponding reference monitoring position point set is averaged and used as the aggregate distribution spacing of each monitoring position point. The aggregate distribution spacing is ratioed to the preset appropriate distribution spacing and the inverse is taken to obtain the aggregate aggregation degree of each monitoring position point. Then, the aggregate aggregation degree of each depth layer corresponding to each monitoring position point is averaged and the aggregate aggregation degree of each depth layer is obtained.

7. The nondestructive testing system for asphalt mixture segregation degree based on digital image processing according to claim 1, characterized in that: The specific method of identifying the depth layer with abnormal aggregate distribution is as follows: The aggregate distribution ratio and aggregate aggregation of each depth layer are compared with the corresponding depth layer benchmark range respectively, and the depth layer whose aggregate distribution ratio or aggregate aggregation exceeds the corresponding depth layer benchmark range is recorded as the depth layer with abnormal aggregate distribution; The same depth layer benchmark range is obtained based on historical samples through confidence interval analysis.

8. The nondestructive testing system for asphalt mixture segregation degree based on digital image processing according to claim 3, characterized in that: The specific method of identifying the low polarization depth layer is as follows: Compare the surface polarization difference values of each aggregate at each depth layer with the preset polarization deviation value threshold, and record the aggregate area where the surface polarization difference value of the aggregate is greater than the corresponding polarization deviation value threshold as a low polarization aggregate area; The number of low-polarization aggregate areas and the total number of aggregate areas in each depth layer are counted, and then the proportion calculation is performed to obtain the proportion of the number of low-polarization areas in each depth layer. The depth layer whose proportion of the number of low-polarization areas is greater than the preset low-polarization area proportion threshold is recorded as a low-polarization depth layer.

9. The nondestructive testing system for asphalt mixture segregation degree based on digital image processing according to claim 1, characterized in that: The specific steps of identifying whether each depth layer is pseudo-segregation are as follows: Match the depth layer with abnormal aggregate distribution with the depth layer with low polarization, and record the successfully matched depth layer as the suspected pseudo-segregation depth layer; The aggregate distribution ratio and aggregate aggregation of each suspected pseudo-segregation depth layer are compared with the corresponding depth layer benchmark range respectively. If in a suspected pseudo-segregation depth layer, only one of the aggregate distribution ratio or aggregate aggregation exceeds the corresponding benchmark range, the suspected pseudo-segregation depth layer is identified as a pseudo-segregation depth layer.

10. The nondestructive testing system for asphalt mixture segregation degree based on digital image processing according to claim 1, characterized in that: The specific method of identifying the cause of separation is as follows: Obtain the contour of each aggregate area, and then use image processing software to obtain the contour perimeter and contour area of each aggregate area; Using the formula DF i =2×(lnL ij / lnA ij ) analysis to obtain the aggregate fractal dimension DF corresponding to each depth layer i , where i represents the depth layer number, i=1,2...I, I represents the number of depth layers, j represents the aggregate area number, j=1,2...J, J represents the number of aggregate areas, L ij A represents the perimeter of the jth aggregate area at the i-th depth layer, ij represents the contour area of the jth aggregate region at the i-th depth layer; The aggregate fractal dimension corresponding to the depth layer of abnormal aggregate distribution that is judged not to be pseudo-segregation is compared with the preset aggregate fractal dimension threshold. If the aggregate fractal dimension of a depth layer of abnormal aggregate distribution is less than or equal to the corresponding aggregate fractal dimension threshold, the cause of segregation of the depth layer of abnormal aggregate distribution is identified as uneven mixing. If the aggregate fractal dimension of a depth layer of abnormal aggregate distribution is greater than or equal to the corresponding aggregate fractal dimension threshold, the cause of segregation of the depth layer of abnormal aggregate distribution is identified as rolling crushing.

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