Method for evaluating skid resistance of colored pavement by adopting digital image structure depth

Abstract

The invention relates to the technical field of road engineering, in particular to a method for evaluating the skid resistance of a colored pavement by adopting digital image structure depth, which comprises the following steps: preparing a colored pavement test piece; carrying out image acquisition on the surface of the color pavement test piece under a natural illumination condition; adjusting the pixel size of the image, calculating the gray value of each pixel by adopting a weighted average method, and converting the color image into a gray image; drawing a three-dimensional curved surface by taking the row number of each gray value in the two-dimensional gray matrix as an x axis, the column number as a y axis and the gray value as a z axis; and calculating the construction depth of the digital image. Based on the digital image processing technology, the method for evaluating the skid resistance of the colored pavement by adopting the digital image structure depth is provided, and compared with a traditional pavement structure depth testing method, the method has the characteristics that the operation is simple, the analysis result is less influenced by human factors, the application range is wider, and the like; the method can be used for monitoring the anti-skid performance of the colored pavement in real time, thereby improving the testing efficiency of the structural depth of the colored pavement.

Description

Technical Field

[0001] The present invention relates to the technical field of road engineering, and particularly to a method for evaluating the skid resistance performance of a colored pavement by constructing depth using digital images. Background Art

[0002] Colored pavements have attracted more and more attention and emphasis due to their performance and aesthetics, and are widely used in places such as road intersections, non-motor vehicle or motor vehicle waiting areas, and tunnel entrances and exits. By using the distinct colors of colored pavements, the lanes can be functionally partitioned, which can not only improve the coordination between the road and the surrounding environment, but also play a role in alerting pedestrians and reducing driver fatigue.

[0003] Skid resistance performance is an important part of pavement performance and is related to driving safety. Accurately evaluating the skid resistance performance of colored pavements is of great significance for controlling the construction quality and maintenance quality of colored pavements. Texture depth is one of the important indicators characterizing pavement skid resistance performance. Existing texture depth test methods include the sand patch method and the vehicle-mounted laser texture depth tester method. Although the sand patch method is simple to operate, the measurement results are greatly affected by human factors; the vehicle-mounted laser texture depth instrument is expensive and complex to operate, and is not suitable as a general test method. Therefore, both of these texture depth test methods have certain deficiencies.

[0004] In recent years, with the continuous development of digital image technology, more and more scholars have begun to use digital image technology to study the texture depth of asphalt pavements. The usual approach is to first take pictures of the pavement for sampling, convert the collected color images into grayscale images, and then use digital image methods such as wavelet filtering for noise reduction, histogram equalization transformation, Fourier transform, and generalized regression neural network to study the texture characteristics of asphalt pavements. The above research methods all take traditional black asphalt pavements as the research object. Since colored pavements have diverse colors, the applicability of these research methods to colored pavements is not clear, especially the applicability to colored thin layer overlays has not been demonstrated. Compared with traditional asphalt pavements, colored thin layer overlays usually use hard aggregates with a particle size of 1 - 5 mm, which is much smaller than the maximum particle size of the aggregates used in traditional asphalt pavements.

[0005] In summary, the existing pavement texture depth test methods have problems such as being greatly affected by human factors or being complex to operate, and the texture depth prediction methods based on digital image technology have problems such as unclear applicability to colored pavements. Therefore, it is necessary to further explore and study new methods for evaluating the skid resistance performance of colored pavements. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method for evaluating the skid resistance performance of colored pavements by constructing depth using digital images, which has the characteristics of simple operation, less influence of analysis results by human factors, and a wider application range. It can be used for real-time monitoring of the skid resistance performance of colored pavements, thereby improving the test efficiency of the texture depth of colored pavements.

[0007] To achieve the above object, the present invention provides the following technical solution: A method for evaluating the skid resistance performance of colored pavements by constructing depth using digital images, comprising the following steps,

[0008] S1. Prepare colored pavement specimens;

[0009] S2. Under natural light conditions, use a digital camera to collect images of the surface of the colored pavement specimens, and select partial area of the colored images for analysis;

[0010] S3. For the selected colored images, use the image processing software Photoshop to adjust the pixel size of the images, and use the MATLAB software to extract the component values of each pixel on the red, green, and blue channels. The weighted average method is used to calculate the gray value of each pixel, and a two-dimensional gray matrix is obtained, thereby converting the colored image into a gray image. The gray value calculation formula is:

[0011] Gray i = 0.299R i + 0.587G i + 0.114B i (1)

[0012] In the formula, Gray i is the gray value of the i-th pixel; R i is the component value of the i-th pixel on the red channel; G i is the component value of the i-th pixel on the green channel; B i is the component value of the i-th pixel on the blue channel;

[0013] S4. Taking the row number where the gray value is located in the two-dimensional gray matrix as the x-axis, the column number where the gray value is located in the two-dimensional gray matrix as the y-axis, and the gray value as the z-axis, draw a three-dimensional surface; construct a plane passing through the point with the maximum gray value and parallel to the xy plane. The average depth of the space enclosed by each point on the three-dimensional surface and the constructed plane is the texture depth of the digital image, and its calculation formula is:

[0014]

[0015] V = ∫∫∫[Gray max - Gray(x, y)]dxdydz (3)

[0016] In the formula, TDdi Construct depth for digital image; V is the volume of the space enclosed by each point on the three-dimensional surface and the constructed plane; A is the projected area of the three-dimensional surface on the xy plane; Gray max is the maximum gray value; Gray(x, y) is the gray value of the pixel corresponding to the coordinates (x, y);

[0017] S5. Use the construct depth of the digital image to evaluate the skid resistance performance of the colored pavement. The greater the construct depth of the digital image, the better the skid resistance performance of the colored pavement.

[0018] Preferably, in step S1, the colored pavement specimen is selected from hot mix colored asphalt mixture specimens and colored thin layer mixture specimens;

[0019] The colored thin layer mixture specimen is obtained by uniformly applying a layer of colored thin layer surfacing material on the surface of the concrete specimen.

[0020] Preferably, in step S2, the selected area of the colored image is a square area with a side length of 22 cm.

[0021] Preferably, in step S3, when using the image processing software Photoshop to adjust the pixel size of the colored image, the pixels of different images are adjusted to the same size.

[0022] Preferably, in step S3, the gray values in the two-dimensional gray matrix are arranged in ascending order to obtain an ordered sequence. Select some head data and tail data in the ordered sequence, and replace them with the average value of all gray values to reduce the influence of noise on the calculated value of the construct depth of the digital image.

[0023] Preferably, the head data accounts for the first 0.25% of the ordered sequence, and the tail data accounts for the last 0.25% of the ordered sequence.

[0024] The present invention provides a method for evaluating the skid resistance performance of a colored pavement using the construct depth of a digital image, and has the following beneficial effects compared with the prior art:

[0025] Based on digital image processing technology, the present invention proposes a method for evaluating the skid resistance performance of a colored pavement using the construct depth of a digital image. The construct depth of the digital image obtained through image processing has a good correlation with the construct depth measured by the sand patch method. Therefore, it can be used to replace the traditional test method for construct depth. Compared with the traditional test method for pavement construct depth, this method has the characteristics of simple operation, less influence of the analysis result by human factors, and wider application range. It can be used for real-time monitoring of the skid resistance performance of the colored pavement, thereby improving the test efficiency of the construct depth of the colored pavement. Description of the Drawings

[0026] The accompanying drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0027] Figure 1 is a diagram of the colored asphalt mixture specimen prepared in Example 1 of the present invention;

[0028] Figure 2 is the colored image after pixel size adjustment in Example 1 of the present invention;

[0029] Figure 3 is the converted grayscale image in Example 1 of the present invention;

[0030] Figure 4 is the three-dimensional surface image of the grayscale value drawn in Example 1 of the present invention;

[0031] Figure 5 is a diagram of the colored thin layer overlay specimen prepared in Example 2 of the present invention;

[0032] Figure 6 is the colored image after pixel size adjustment in Example 2 of the present invention;

[0033] Figure 7 is the converted grayscale image in Example 2 of the present invention;

[0034] Figure 8 is the three-dimensional surface image of the grayscale value drawn in Example 2 of the present invention;

[0035] Figure 9 is a diagram showing the relationship between the digital image texture depth of each specimen of the present invention and the texture depth measured by the sand patch method. Detailed Embodiments

[0036] The following embodiments are used to detail the implementation manners of the present application, so as to fully understand the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects and implement accordingly.

[0037] Example 1

[0038] A method for evaluating the skid resistance performance of a colored pavement using digital image texture depth includes the following steps:

[0039] S1. Prepare the hot mix colored asphalt mixture specimen A; refer to the "Test Regulations for Asphalt and Asphalt Mixtures in Highway Engineering" (JTG E20-2011) and the "Construction Technical Specifications for Highway Asphalt Pavements" (JTG F40-2004) to conduct the mix design of the hot mix colored asphalt mixture. The mass ratio of each component of the hot mix colored asphalt mixture specimen A is colored asphalt A: basalt aggregate: limestone mineral powder: red iron oxide pigment = 4.56:91.66:1.89:1.89. Among them, colored asphalt A is a high-viscosity and high-elasticity colored modified asphalt from Xiamen Xinlijin Co., Ltd. Then prepare the hot mix colored asphalt mixture specimen A with a length of 300 mm, a width of 300 mm, and a height of 50 mm, as Figure 1 shown.

[0040] S2. Place the digital camera at a height of 50 cm from the surface of the specimen, and collect the image of the surface of the hot mix colored asphalt mixture specimen A under natural light conditions. Then, intercept a square area with a side length of 22 cm from the collected color image for subsequent analysis, and make the area of the image acquisition area as close as possible to the circular area paved by the sand patch method.

[0041] S3. For the selected color image, use the image processing software Photoshop to adjust the pixel size of the image to 2000×2000. The adjusted color image is as Figure 2 shown. Use the MATLAB software to extract the component values of each pixel in the red, green, and blue channels, calculate the gray value of each pixel by the weighted average method shown in formula (1), and obtain a two-dimensional gray matrix. Arrange the gray values in the matrix in ascending order to get an ordered sequence. Select the first 0.25% data and the last 0.25% data in the ordered sequence, and replace them with the average value of all gray values to reduce the influence of image noise on the calculated value of the digital image structure depth, so as to convert the color image into a gray image, as Figure 3 shown.

[0042] Gray i = 0.299R i + 0.587G i + 0.114B i (1)

[0043] In the formula, Gray i is the gray value of the i-th pixel; R i is the component value of the i-th pixel in the red channel; G i is the component value of the i-th pixel in the green channel; B i is the component value of the i-th pixel in the blue channel;

[0044] S4. Taking the row number where the gray value is located in the two-dimensional gray matrix as the x-axis, the column number where the gray value is located in the two-dimensional gray matrix as the y-axis, and the gray value as the z-axis, draw a three-dimensional surface, as Figure 4 shown; construct a plane passing through the point with the maximum gray value and parallel to the xy plane. The average depth of the space enclosed by each point on the three-dimensional surface and the constructed plane is the digital image construction depth, and its calculation method is as shown in formulas (2) and (3):

[0045]

[0046] V = ∫∫∫[Gray max - Gray(x, y)]dxdydz (3)

[0047] In the formula, TD di is the digital image construction depth; V is the volume of the space enclosed by each point on the three-dimensional surface and the constructed plane; A is the projected area of the three-dimensional surface on the xy plane; Gray max is the maximum gray value; Gray(x, y) is the gray value of the pixel corresponding to the coordinates (x, y);

[0048] S5. Use the digital image construction depth to evaluate the skid resistance performance of the colored pavement. The larger the digital image construction depth, the better the skid resistance performance of the colored pavement.

[0049] In this embodiment, the digital image construction depth of the surface of the hot mix colored asphalt mixture specimen A is calculated to be 107.54.

[0050] Example 2

[0051] A method for evaluating the skid resistance performance of a colored pavement using the digital image construction depth is basically the same as that in Example 1, except that: the hot mix colored asphalt mixture specimen A is replaced with a colored thin layer mixture specimen A, as Figure 5 shown, the colored image after adjusting the pixel size is as Figure 6 shown, the converted gray image is as Figure 7 shown, and the drawn three-dimensional surface image of the gray value is as Figure 8 shown.

[0052] The above-mentioned colored thin layer mixture specimen A is prepared by evenly applying a colored thin layer overlay material on a concrete specimen (asphalt mixture rutting plate) with a length of 300 mm, a width of 300 mm, and a height of 50 mm. Among them, the colored thin layer overlay material is obtained by fully mixing methyl methacrylate (MMA) binder, colloidal amine curing agent, and quartz sand (particle size ≤ 4.75 mm) according to a mass ratio of 25:0.5:10.

[0053] In this embodiment, the digital image construction depth of the surface of the colored thin layer mixture specimen A is calculated to be 86.04.

[0054] Example 3

[0055] A method for evaluating the skid resistance performance of colored pavement by using digital image texture depth is basically the same as that of Example 1, except that: the hot mix colored asphalt mixture specimen A is replaced by hot mix colored asphalt mixture B, and the mass ratio of its components is colored asphalt B: basalt aggregate: limestone powder: red iron oxide pigment = 4.56: 91.66: 1.89: 1.89; among them, colored asphalt B is a special colored modified asphalt from Xiamen Xinlijin Co., Ltd.

[0056] The digital image texture depth of the surface of the hot mix colored asphalt mixture specimen B calculated in this example is 100.75.

[0057] Example 4

[0058] A method for evaluating the skid resistance performance of colored pavement by using digital image texture depth is basically the same as that of Example 1, except that: the hot mix colored asphalt mixture specimen A is replaced by colored thin layer mixture specimen B.

[0059] Preparation of colored thin layer mixture specimen B: The gray MMA binder, powdery acyl curing agent, and emery (particle size ≤ 1.18 mm) are fully mixed according to the mass ratio of 25: 0.15: 1, and then the obtained colored thin layer surfacing material is evenly applied on a concrete specimen (asphalt mixture rutting plate) with a length of 300 mm, a width of 300 mm, and a height of 50 mm. Finally, the blue MMA binder is evenly applied on the rutting plate to obtain it.

[0060] The digital image texture depth of the surface of the colored thin layer mixture specimen B calculated in this example is 45.85.

[0061] Example 5

[0062] A method for evaluating the skid resistance performance of colored pavement by using digital image texture depth is basically the same as that of Example 1, except that: the hot mix colored asphalt mixture specimen is replaced by colored thin layer mixture specimen C.

[0063] Preparation of colored thin layer mixture specimen C: The gray MMA binder, powdery acyl curing agent, and emery (particle size ≤ 1.18 mm) are fully mixed according to the mass ratio of 25: 0.15: 1, and then the obtained colored thin layer surfacing material is evenly applied on a concrete specimen (asphalt mixture rutting plate) with a length of 300 mm, a width of 300 mm, and a height of 50 mm. Finally, the gray MMA binder is evenly applied on the rutting plate to obtain it.

[0064] The digital image texture depth calculated for the surface of the colored thin-layer mixture specimen C in this embodiment is 48.06.

[0065] Example 6

[0066] A method for evaluating the skid resistance performance of colored pavements using digital image texture depth is basically the same as that in Example 1, except that: the hot mix colored asphalt mixture specimen is replaced with a colored thin-layer mixture specimen D.

[0067] Preparation of the colored thin-layer mixture specimen D: The red MMA binder, powdered amine curing agent, and quartz sand (particle size ≤ 4.75 mm) are thoroughly mixed in a mass ratio of 25:0.3:14, and then the obtained colored thin-layer surfacing material is evenly applied to a concrete specimen (asphalt mixture rutting plate) with a length of 300 mm, a width of 300 mm, and a height of 50 mm, thus obtaining it.

[0068] The digital image texture depth calculated for the surface of the colored thin-layer mixture specimen D in this embodiment is 83.09.

[0069] Verification

[0070] Referring to the "Code for Field Tests of Highway Subgrade and Pavement" (JTG 3450-2019), the texture depth of the surfaces of the six specimens in the above Examples 1-6 was measured using the sand patch method, and the test results were 1.146 mm, 0.945 mm, 0.982 mm, 0.593 mm, 0.632 mm, and 0.966 mm respectively. Then, the relationship between the digital image texture depth and the texture depth measured by the sand patch method was established, as Figure 9 shown. It can be Figure 9 obtained that there is a strong correlation between the digital image texture depth and the texture depth measured by the sand patch method, and the determination coefficient reaches 0.9577. This shows that the digital image texture depth can be used as an index to accurately evaluate the skid resistance performance of colored pavements.

[0071] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the anti-skid performance of a colored pavement using a digital image construction depth, characterized in that: The following steps are involved: S1. Prepare color road test specimens; S2. Under natural light conditions, use a digital camera to collect images of the surface of the color road test piece, and select color images of a part of the area for analysis; S3. For the selected color image, the pixel size of the image is adjusted using image processing software, and the component values ​​of each pixel in the red, green, and blue channels are extracted using MATLAB software. The grayscale value of each pixel is calculated using the weighted average method to obtain a two-dimensional grayscale matrix, thereby converting the color image into a grayscale image. The grayscale value calculation formula is: Gray i =0.299R i +0.587G i +0.114B i (1) In the formula, Gray i is the gray value of the i-th pixel; R i is the component value of the i-th pixel on the red channel; G i is the component value of the i-th pixel on the green channel; β i is the component value of the i-th pixel in the blue channel; S4. Draw a three-dimensional surface with the number of rows where the grayscale value is located in the two-dimensional grayscale matrix as the x-axis, the number of columns where the grayscale value is located in the two-dimensional grayscale matrix as the y-axis, and the grayscale value as the z-axis; construct a plane that passes through the point with the maximum grayscale value and is parallel to the xy plane. The average depth of the space enclosed by each point on the three-dimensional surface and the constructed plane is the digital image construction depth, and its calculation formula is: V=∫∫∫[Gray ma x-Gray(x,y)]dxdydz (3) In the formula, TD di is the depth of the digital image; V is the volume of the space enclosed by each point on the three-dimensional surface and the constructed plane; A is the projection area of ​​the three-dimensional surface on the xy plane; Gray max is the maximum gray value; Gray(x, y) is the gray value of the pixel corresponding to the coordinate (x, y); S5. The anti-skid performance of the colored pavement is evaluated by using the digital image construction depth. The greater the digital image construction depth, the better the anti-skid performance of the colored pavement.

2. The method for evaluating the anti-skid performance of a colored pavement by using digital image construction depth according to claim 1, characterized in that: In step S1, the colored road test specimen is selected from a hot-mix colored asphalt mixture specimen and a colored thin layer mixture specimen; The colored thin layer mixture specimen is prepared by evenly applying a layer of colored thin layer covering material on the surface of a concrete specimen.

3. The method for evaluating the anti-skid performance of a colored pavement by using digital image construction depth according to claim 1, characterized in that: In step S2, the area of ​​the color image is selected as a square area with a side length of 22 cm.

4. The method for evaluating the anti-skid performance of a colored pavement by using digital image construction depth according to claim 1, characterized in that: In step S3, when the pixel size of the color image is adjusted using the image processing software, the pixels of different images are adjusted to the same size.

5. The method for evaluating the anti-skid performance of a colored pavement by using digital image construction depth according to claim 1, characterized in that: In step S3, the grayscale values ​​in the two-dimensional grayscale matrix are arranged in ascending order to obtain a set of ordered series, and some head data and tail data in the ordered series are selected and replaced with the average value of all grayscale values ​​to reduce the influence of noise on the depth calculation value of digital image construction.

6. The method for evaluating the anti-skid performance of a colored pavement by using digital image construction depth according to claim 5, characterized in that: The head data occupies the first 0.25% of the ordered sequence, and the tail data occupies the last 0.25% of the ordered sequence.

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