Coarse aggregate three-dimensional morphology characteristic evaluation method and detection device

Abstract

The invention discloses a method for evaluating three-dimensional morphological characteristics of coarse aggregates. The method comprises the following steps: extracting and sampling crushed finished coarse aggregates; washing and drying the extracted coarse aggregate, placing the coarse aggregate on an automatic turntable, and keeping the coarse aggregate stable without rolling; starting a three-dimensional morphology feature detection device, calibrating a camera, setting scanning times, point cloud grid density and a model alignment method, and continuously and automatically acquiring three-dimensional point cloud data of the coarse aggregate in each direction by the three-dimensional morphology feature detection device; after scanning is finished, three-dimensional point cloud data of the coarse aggregate is automatically transmitted to an analysis computer, and a corresponding three-dimensional model, namely morphological characteristic parameters, is obtained through three-dimensional shape reconstruction and automatic reconstruction of an analysis algorithm. According to the method, through a set of device and method integrating three-dimensional shape scanning and characteristic index calculation, the detection accuracy and efficiency are improved, and the operation process is simplified, so that high-efficiency and high-precision detection and evaluation of the road coarse aggregate three-dimensional shape characteristics are met.

Description

Technical Field

[0001] The invention relates to the field of road engineering, and in particular to a method and a detection device for evaluating three-dimensional morphological characteristics of coarse aggregate. Background Art

[0002] In existing highway projects, aggregate is the most widely used type of pavement material, accounting for more than 90% of the total mass of asphalt mixtures, of which coarse aggregate accounts for more than 70%. It determines the contact and interlocking ability between particles in the skeleton structure of asphalt concrete and the texture of the road surface, which in turn affects the mechanical strength of asphalt mixtures, the anti-skid performance of the road surface and other road performance. Therefore, the detection and evaluation of the morphological characteristics of coarse aggregates have always received great attention.

[0003] The morphological characteristics of coarse aggregate can be divided into shape, angular characteristics and surface texture roughness according to its size. In the past, the detection and evaluation methods mostly used two-dimensional images to identify the characteristics of coarse aggregate. However, the two-dimensional images are mainly based on the method of taking photos, which is easy to miss the three-dimensional information of the aggregate. The morphological characteristic parameters obtained are mostly two-dimensional shape and angularity indicators, which cannot fully characterize the three-dimensional morphological characteristics of coarse aggregate. Therefore, in recent years, some researchers have adopted X-ray laser or line laser scanning methods to obtain the morphological characteristics of coarse aggregate. The three-dimensional model obtained by X-ray laser can roughly restore the three-dimensional morphology of coarse aggregate. Although it can effectively evaluate the three-dimensional shape and angular characteristics of coarse aggregate, the micro-texture roughness cannot be accurately obtained; while the use of line laser scanning can accurately obtain the three-dimensional morphological characteristic parameters, but it is mainly for the evaluation of the micro-geometric characteristics of the surface contour. The micro-roughness index obtained is also a line roughness parameter, which cannot accurately characterize the micro-texture roughness of the entire coarse aggregate surface. Structured light scanning technology has good development prospects due to its high scanning efficiency and precision, and it has the characteristics of line laser scanning and can be used to calculate surface roughness. Therefore, by using the three-dimensional model of coarse aggregate obtained by structured light scanning, research on the evaluation of the three-dimensional morphological characteristics of coarse aggregate can be carried out based on the high-precision three-dimensional morphology. Summary of the invention

[0004] In view of the above problems, the present invention aims to provide a device with high integration, high precision, high efficiency, stable and reliable indoor detection of three-dimensional morphological characteristics of aggregates, and proposes a method for evaluating the three-dimensional morphological characteristics of road coarse aggregate, which can be used to analyze the relationship between the three-dimensional morphological characteristics of aggregates and road performance.

[0005] To achieve the technical purpose, the solution of the present invention is: a method for evaluating the three-dimensional morphological characteristics of coarse aggregate, the specific steps are as follows: S1. Extract and sample the finished coarse aggregate after crushing; S2. Wash and dry the extracted coarse aggregate, and place it on an automatic turntable to keep the coarse aggregate stable and not rolling; S3, turning on the three-dimensional shape feature detection device, calibrating the camera, setting the number of scans, point cloud grid density, and model alignment method, and the three-dimensional shape feature detection device continuously and automatically acquires three-dimensional point cloud data in all directions of the coarse aggregate; S4. After scanning, the three-dimensional point cloud data of the coarse aggregate is automatically transmitted to the analysis computer, and automatically reconstructed through three-dimensional morphology reconstruction and analysis algorithm to obtain the corresponding three-dimensional model, namely the morphological characteristic parameters.

[0006] Preferably, in step S4, the sphericity S, ellipsoidality E, and steepness S of the aggregate are calculated by analyzing the three-dimensional morphology feature analysis algorithm of the computer. ku、 Arithmetic mean height S a and the RMS height S q ; The sphericity S is the ratio of the surface area of ​​a sphere of the same volume to the actual surface area of ​​the aggregate; the ellipsoidality E is defined as the ratio of the actual volume of the aggregate to the volume of a sphere of the same volume; the arithmetic mean height S a It is the average value of the sum of the absolute values ​​of the differences between the elevation of each point and the average value in the three-dimensional model; the root mean square height S q is the root mean square of the height of each point in the three-dimensional model; steepness S ku is the texture roughness, shape, and sharpness of the area.

[0007] Preferably, in step S4, the calculation formula of the sphericity S is: ; Where S1 is the surface area of ​​the aggregate, and S2 is the surface area of ​​a sphere of the same volume as the aggregate particles; The calculation formula of ellipsoid degree is: ; Where V1 is the volume of coarse aggregate particles, V2 is the volume of the minimum circumscribed triaxial ellipsoid of the aggregate; During the analysis, the three-dimensional model is discretized into a grid composed of triangles through the triangulation algorithm. The three-dimensional morphology feature analysis algorithm in the analysis computer traverses each triangle in the grid to calculate its area and volume. The area and volume of each triangle are added together to obtain the aggregate surface area S1 and V1. The corresponding surface area of ​​a sphere with the same volume is At the same time, by fitting a triaxial unequal ellipsoid to the three-dimensional solid model of coarse aggregate, shrinking the ellipsoid boundary, the triaxial radii a, b, c when the ellipsoid volume is the smallest are obtained, and then the volume of the minimum circumscribed triaxial ellipsoid of coarse aggregate is calculated as . .

[0008] Preferably, in step S4, the arithmetic mean height S a The calculation formula is: ; The root mean square height S q The calculation formula is: ; The calculation formula of steepness is: ; Where A is the sampling area and z(x,y) is the height at the spatial position (x,y) in the three-dimensional model.

[0009] A detection device, comprising a device body for detecting three-dimensional morphological characteristics of coarse aggregate, using a coarse aggregate three-dimensional morphological characteristics evaluation method, the device body comprising a hardware control module integrating camera and structured light emitter control functions, and an analysis computer (2) for three-dimensional morphological reconstruction and analysis algorithms; The device body also includes a scanning system and a coarse aggregate testing platform (1); the scanning system includes a structured light emitter (3), a left camera (4) and a right camera (5); The three-dimensional shape reconstruction and analysis algorithm includes the phase shift fringes of structured light and the three-dimensional reconstruction related algorithm, and the automatic calculation algorithm of the three-dimensional shape of coarse aggregate; The coarse aggregate testing platform (1) is an automatic turntable.

[0010] Preferably, the structured light transmitter (3) is provided with a fully digital video and sound transmission interface, and the left camera (4) and the right camera (5) are provided with USB interfaces, which are connected to the analysis computer (2) via a data transmission line (6); The structured light emitter (3) is fixed on the backplane bracket and is located between the left camera (4) and the right camera (5), and the three are synchronously triggered and controlled by a hardware control module; The hardware control module, three-dimensional morphology reconstruction and analysis algorithm can adjust the scanning parameters on the analysis computer (2).

[0011] Preferably, the scanning system projects a grating onto the coarse aggregate via a structured light transmitter, receives the grating via a left camera (4) and a right camera (5), and obtains position information of each point on the grating based on the principle of triangulation.

[0012] Preferably, the hardware control module controls the projection of structured light, the phase shift of the projection grating and the synchronous acquisition of the camera by combining the three-dimensional morphology reconstruction and analysis algorithm.

[0013] Preferably, the three-dimensional morphology scan of the coarse aggregate is combined with reference points to form an overdetermined set of equations for each point of the grating projection, and the least squares solution is obtained through software to obtain the three-dimensional point cloud data of the coarse aggregate and realize the reconstruction of the three-dimensional model.

[0014] Preferably, the automatic turntable (1) is placed directly below the structured light emitter (3) and is connected to the analysis computer (2) via a remote control USB flash drive (7), thereby enabling the hardware control module to control the full-angle rotation of the coarse aggregate.

[0015] The beneficial effects of the present invention are as follows: the method of the present application improves the accuracy and efficiency of detection and simplifies the operation process through a set of devices and methods integrating three-dimensional morphology scanning and characteristic index calculation, so as to meet the requirements of high-efficiency and high-precision detection and evaluation of the three-dimensional morphology characteristics of coarse aggregate for road use. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the structure of a device for detecting three-dimensional morphological characteristics of road coarse aggregate in an embodiment of the present invention; Figure 2 It is a schematic diagram of the process of the present invention; Figure 3 This is a three-dimensional model diagram of some coarse aggregate particles automatically reconstructed by the analysis computer in an embodiment of the present invention.

[0017] Explanation of the accompanying drawings: 1. Coarse aggregate testing platform, i.e., automatic turntable; 2. Analysis computer; 3. Structured light transmitter; 4. Left camera; 5. Right camera; 6. Data transmission line; 7. Remote control USB flash drive. DETAILED DESCRIPTION

[0018] The following will be combined with the attached embodiment of the present invention Figure 1-3 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Reference Figure 1 As shown, the present invention provides a three-dimensional shape detection device and evaluation method for coarse aggregate for road use, including a coarse aggregate three-dimensional shape feature detection device, the device includes a scanning system, a coarse aggregate test platform 1, and an analysis computer 2. The scanning system includes a structured light emitter 3, a left camera 4 and a right camera 5. The structured light emitter 3 is fixed on a backplane bracket and is located between the left camera 4 and the right camera 5. The structured light emitter 3 projects a grating onto the coarse aggregate, which is received by the left camera 4 and the right camera 5, and the position information of each point of the grating is obtained according to the principle of triangulation. The coarse aggregate test platform, i.e., an automatic turntable 1, is located directly below the structured light emitter 3 and is connected to an analysis computer through a remote control U disk 7 to realize the control of the full-angle rotation of the coarse aggregate by a hardware control module. The software module of the analysis computer 2 integrates the hardware control module, the three-dimensional shape reconstruction and the analysis algorithm. After the scanning is completed, the three-dimensional point cloud data of the aggregate is automatically transmitted to the analysis computer, and the three-dimensional model is automatically reconstructed and the three-dimensional shape feature index is calculated through the algorithm module.

[0021] Table 1: The performance parameters of the scanning system in this device are shown in the following table.

[0022] Measuring accuracy (mm) Single-sided scanning speed (s) Single-sided scanning points (pixels) Average point distance (mm) 0.001～0.05 1-3 6.3 million 0.005～0.1 Reference Figure 2 As shown, the present invention provides a three-dimensional morphology detection device and evaluation method for road coarse aggregate, and also includes a three-dimensional morphology feature evaluation method for road coarse aggregate, the evaluation method comprising the following steps: S1. Sampling of coarse aggregate for road use. For new construction projects, sampling of finished products after crushing and processing is carried out, while for operating roads, sampling is carried out by extraction; S2. After the coarse aggregate is washed and dried, it is placed on an automatic turntable to prevent the aggregate from rolling; S3, start the three-dimensional shape feature detection device, calibrate the camera, set the number of scans, point cloud grid density, and model alignment method, and the device continuously and automatically obtains three-dimensional point cloud data in all directions of the aggregate; S4. After scanning, the aggregate 3D point cloud data is automatically transmitted to the analysis computer, and the 3D model is automatically reconstructed through 3D shape reconstruction and analysis algorithms; S5. Calculate the sphericity, ellipsoidity, arithmetic mean height and root mean square height of the aggregate by analyzing the computer's three-dimensional morphology feature analysis algorithm according to the following formula. Calculate the sphericity S, ellipsoidity E, steepness S of the aggregate by analyzing the computer's three-dimensional morphology feature analysis algorithm ku、 Arithmetic mean height S a and the RMS height S q ; Among them, the sphericity S is the ratio of the surface area of ​​a sphere of the same volume of aggregate to the actual surface area of ​​the aggregate. The larger the ratio, the stronger the aggregate's ability to resist crushing; the ellipsoidality E is defined as the ratio of the actual volume of the aggregate to the volume of a sphere of the same volume. The larger the ratio, the worse the angularity of the aggregate; the arithmetic mean height S a It is the average value of the sum of the absolute values ​​of the differences between the elevation of each point in the three-dimensional model and the average value. The larger the value, the coarser the aggregate. q It is the root mean square of the height of each point in the three-dimensional model, which is equivalent to the standard deviation of the height. The larger the value, the rougher the aggregate surface. ku It is the texture roughness, shape and sharpness of the area. The larger the value, the sharper the roughness and shape.

[0023] The calculation formula of sphericity S is: ; Where S1 is the surface area of ​​the aggregate, and S2 is the surface area of ​​a sphere of the same volume as the aggregate particles; The calculation formula of ellipsoid degree is: ; Where V1 is the volume of coarse aggregate particles, V2 is the volume of the minimum circumscribed triaxial ellipsoid of the aggregate; During the analysis, the three-dimensional model is discretized into a grid composed of triangles through the triangulation algorithm. The three-dimensional morphology feature analysis algorithm in the analysis computer traverses each triangle in the grid to calculate its area and volume. The area and volume of each triangle are added together to obtain the aggregate surface area S1 and V1. The corresponding surface area of ​​a sphere with the same volume is At the same time, by fitting a triaxial unequal ellipsoid to the three-dimensional solid model of coarse aggregate, shrinking the ellipsoid boundary, the triaxial radii a, b, c when the ellipsoid volume is the smallest are obtained, and then the volume of the minimum circumscribed triaxial ellipsoid of coarse aggregate is calculated as .

[0024] The arithmetic mean height S a The calculation formula is: ; The root mean square height S q The calculation formula is: ; The calculation formula of steepness is: ; Where A is the sampling area, and z (x, y) is the height at the spatial position (x, y) in the three-dimensional model, in mm.

[0025] In this embodiment, a relatively dim light environment is selected indoors to perform three-dimensional morphological feature detection on the coarse aggregate.

[0026] In this embodiment, the scanning system has good recognition and analysis capabilities for the features of the scanned object. In the scanning of coarse aggregate, it only needs to perform automatic scanning according to the set number of scans, cloud grid density, and model alignment method, and realize automatic splicing based on the feature judgment of the coarse aggregate.

[0027] In this embodiment, during the coarse aggregate scanning process, some fine debris of the previous batch of coarse aggregate will remain on the automatic turntable. Therefore, after the model is automatically spliced, it is necessary to carefully check the model quality and delete the redundant point cloud data other than the coarse aggregate particles themselves.

[0028] Table 2: Summary of ten groups of coarse aggregate morphological characteristic parameters.

[0029] Group No. <![CDATA[Surface area S1 / mm 2 > <![CDATA[Volume V1 / mm 3 > Sphericity S Ellipsoid E <![CDATA[Arithmetic mean height S a > <![CDATA[Root mean square height S q > <![CDATA[Steepness S ku > 1 561.239 1020.196 1.145 0.643 1.320 1.625 3.013 2 805.943 1612.190 1.212 0.698 2.267 2.757 2.661 3 782.667 1711.504 1.131 0.793 1.461 1.878 3.927 4 715.373 1454.021 1.153 0.785 3.070 3.771 2.636 5 628.157 1134.658 1.194 0.639 3.577 4.273 2.350 6 726.653 1428.830 1.184 0.644 1.836 2.350 3.571 7 686.968 1332.585 1.173 0.611 1.592 2.099 4.064 8 755.281 1587.860 1.147 0.733 1.494 1.847 2.859 9 777.285 1628.605 1.161 0.926 1.768 2.272 3.250 10 874.859 1939.606 1.163 0.894 1.410 1.808 3.461

[0030] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any slight modification, equivalent substitution and improvement made to the above embodiment based on the technical essence of the present invention should be included in the protection scope of the technical solution of the present invention.

Claims

1. A method for evaluating the three-dimensional morphological characteristics of coarse aggregate, characterized in that: The specific steps are as follows: S1. Extract and sample the finished coarse aggregate after crushing; S2. Wash and dry the extracted coarse aggregate, and place it on an automatic turntable to keep the coarse aggregate stable and not rolling; S3, turning on the three-dimensional shape feature detection device, calibrating the camera, setting the number of scans, point cloud grid density, and model alignment method, and the three-dimensional shape feature detection device continuously and automatically acquires three-dimensional point cloud data in all directions of the coarse aggregate; S4. After scanning, the three-dimensional point cloud data of the coarse aggregate is automatically transmitted to the analysis computer, and automatically reconstructed through three-dimensional morphology reconstruction and analysis algorithm to obtain the corresponding three-dimensional model, namely the morphological characteristic parameters.

2. The method for evaluating three-dimensional morphological characteristics of coarse aggregate according to claim 1, characterized in that: In step S4, the sphericity S, ellipsoidality E, and steepness S of the aggregate are calculated by analyzing the three-dimensional morphology feature analysis algorithm of the computer. ku、 Arithmetic mean height S a and the RMS height S q ; Wherein the sphericity S is the ratio of the surface area of ​​a sphere of the same volume of aggregate to the actual surface area of ​​the aggregate; Ellipsoidity E is defined as the ratio of the actual volume of the aggregate to the volume of a sphere of the same volume; the arithmetic mean height S a It is the average value of the sum of the absolute values ​​of the differences between the elevation of each point and the average value in the three-dimensional model; the root mean square height S q is the root mean square of the height of each point in the three-dimensional model; steepness S ku is the texture roughness, shape, and sharpness of the area.

3. The method for evaluating three-dimensional morphological characteristics of coarse aggregate according to claim 2, characterized in that: In step S4, the calculation formula of sphericity S is: ; Where S1 is the surface area of ​​the aggregate, and S2 is the surface area of ​​a sphere of the same volume as the aggregate particles; The calculation formula of ellipsoidality is: ; Where V1 is the volume of coarse aggregate particles, V2 is the volume of the minimum circumscribed triaxial ellipsoid of the aggregate; During the analysis, the three-dimensional model is discretized into a grid composed of triangles through the triangulation algorithm. The three-dimensional morphology feature analysis algorithm in the analysis computer traverses each triangle in the grid to calculate its area and volume. The area and volume of each triangle are added together to obtain the aggregate surface area S1 and V1. The corresponding surface area of ​​a sphere with the same volume is At the same time, by fitting a triaxial unequal ellipsoid to the three-dimensional solid model of coarse aggregate, shrinking the ellipsoid boundary, the triaxial radii a, b, c when the ellipsoid volume is the smallest are obtained, and then the volume of the minimum circumscribed triaxial ellipsoid of coarse aggregate is calculated as .

4. The method for evaluating three-dimensional morphological characteristics of coarse aggregate according to claim 2, characterized in that: In step S4, the arithmetic mean height S a The calculation formula is: ; The root mean square height S q The calculation formula is: ; The calculation formula of steepness is: ; Where A is the sampling area and z(x,y) is the height at the spatial position (x,y) in the three-dimensional model.

5. A detection device, comprising a device body for detecting three-dimensional morphological characteristics of coarse aggregate, characterized in that: The method for evaluating the three-dimensional morphological characteristics of coarse aggregate according to any one of claims 1 to 4 is adopted, wherein the device body comprises a hardware control module integrating the control functions of a camera and a structured light emitter, and an analysis computer (2) for three-dimensional morphological reconstruction and analysis algorithms; The device body also includes a scanning system and a coarse aggregate testing platform (1); the scanning system includes a structured light emitter (3), a left camera (4) and a right camera (5); The three-dimensional shape reconstruction and analysis algorithm includes the phase shift fringes of structured light and the three-dimensional reconstruction related algorithm, and the automatic calculation algorithm of the three-dimensional shape of coarse aggregate; The coarse aggregate testing platform (1) is an automatic turntable.

6. A road coarse aggregate three-dimensional shape detection device and evaluation method according to claim 5, characterized in that: The structured light transmitter (3) is provided with a fully digital video and sound transmission interface, and the left camera (4) and the right camera (5) are provided with USB interfaces, which are connected to the analysis computer (2) via a data transmission line (6); The structured light emitter (3) is fixed on the backplane bracket and is located between the left camera (4) and the right camera (5), and the three are synchronously triggered and controlled by a hardware control module; The hardware control module, three-dimensional morphology reconstruction and analysis algorithm can adjust the scanning parameters on the analysis computer (2).

7. A road coarse aggregate three-dimensional shape detection device and evaluation method according to claim 6, characterized in that: The scanning system projects a grating onto the coarse aggregate through a structured light transmitter, receives the grating through a left camera (4) and a right camera (5), and obtains position information of each point of the grating based on the principle of triangulation.

8. A road coarse aggregate three-dimensional shape detection device and evaluation method according to claim 7, characterized in that: The hardware control module controls the projection of structured light, the phase shift of the projection grating and the synchronous acquisition of the camera by combining the three-dimensional morphology reconstruction and analysis algorithm.

9. A road coarse aggregate three-dimensional shape detection device and evaluation method according to claim 6, characterized in that: The three-dimensional morphology scan of the coarse aggregate will be combined with the reference point to form an overdetermined set of equations for each point of the grating projection, and the least squares solution will be obtained through software to obtain the three-dimensional point cloud data of the coarse aggregate and realize the reconstruction of the three-dimensional model.

10. A road coarse aggregate three-dimensional shape detection device and evaluation method according to claim 5, characterized in that: The automatic turntable (1) is placed directly below the structured light emitter (3) and is connected to the analysis computer (2) via a remote control USB disk (7), thereby enabling the hardware control module to control the full-angle rotation of the coarse aggregate.

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