CN118134937B - Large-gap asphalt pavement structure depth detection method based on image processing - Google Patents
Large-gap asphalt pavement structure depth detection method based on image processing Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 53
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- 238000010276 construction Methods 0.000 claims abstract description 55
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- 239000011800 void material Substances 0.000 claims description 29
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Abstract
The invention provides a large-gap asphalt pavement construction depth detection method based on image processing, which is characterized in that road surface gap edges are identified based on three-dimensional point cloud data and image processing technology, a large-gap asphalt pavement surface gap volume calculation model is established, the area formed in a road surface after filling the road surface gap by a volume threshold is determined through volume threshold setting, the construction depth is obtained according to a construction depth calculation method in a standard, the detection result completely corresponds to a method required by the standard, and the method can be directly used for road surface assessment, acceptance inspection and scientific, objective and effective evaluation of road surface conditions. The invention provides the method for detecting the depth of the large-gap asphalt pavement structure, which has high detection precision, high speed and no pollution for the depth process of the large-gap asphalt pavement structure, can judge the depth distribution condition of the pavement structure, is convenient for timely improvement in the construction process, provides theoretical basis for pavement disease cause analysis, construction condition assessment, engineering acceptance and the like, and has simple technical content operation and easy realization.
Description
Technical Field
The invention belongs to the technical field of image processing vision recognition, and particularly relates to a large-gap asphalt pavement structure depth detection method based on image processing.
Background
The large-gap asphalt pavement is characterized in that coarse aggregate is more and generally accounts for more than 85%, the void ratio is 15% -25%, rainwater is rapidly discharged from the pavement range through the communicating void in the structure of the large-gap asphalt pavement, so that accumulated water on the pavement is eliminated, the large-gap asphalt pavement is also called a porous anti-slip layer or multi-gap asphalt pavement, the large-gap asphalt pavement is more in coarse aggregate, steel wheels are generally adopted for rolling in the construction process, larger voids are formed on the surface after rolling, the anti-slip performance is better, the water permeable effect after construction is ensured, and therefore, the surface texture condition of the large-gap surface is an important index for evaluating the construction quality and the anti-slip drainage function of the large-gap asphalt pavement.
The construction depth of the asphalt pavement is listed as an important index for engineering evaluation in the highway engineering quality verification evaluation standard (JTG F80/1) and the highway asphalt pavement construction technical specification (JTG F40), the highway asphalt pavement construction technical specification (JTGF 40) provides a manual sanding method, an electric sanding instrument and a laser construction depth instrument for detecting the construction depth of the asphalt pavement, the highway engineering quality verification evaluation standard (JTG F80/1) provides a condition that the construction depth in the asphalt pavement acceptance engineering is based on the sanding method detection result, the laser construction depth instrument measures the surface of the ground material particles and the depth change among the material particles by utilizing the principle of laser ranging, the measured result needs to be converted into the sanding method index by carrying out a correlation test, the sanding method index has larger error, and the equipment is not suitable for road surface damaged road sections and has limited use.
Many construction depth detection methods have also been proposed in China, such as: full-width construction depth detection method based on precise three-dimension, publication (bulletin) number: CN116732852a, using a three-dimensional measuring sensor to measure the road surface high-rise and gray data, establishing a three-dimensional point cloud model, combining with a preset construction depth calculation model, defining a region range, and determining the full-width construction depth, where the above patent does not explicitly construct a depth model, the void volume in the defined region range is difficult to identify through the three-dimensional model, and the calculation result cannot be correlated with the standard specification index. A pavement structure depth detection method based on laser vision discloses (announces) the number: CN116716778a, using a laser vision device to obtain data of a detection area, performing data analysis through a preset texture structure model, constructing a three-dimensional model, and identifying a corresponding structure depth, where the patent does not have a clear structure depth identification method and an evaluation model analysis method for the structure depth, the texture three-dimensional model and the zone bit information, and cannot verify the structure depth detection result, and does not have a clear structure depth detection range void volume identification method, and the relationship between the structure depth detection value and the standard value is also not clear.
The existing methods such as laser, image and three-dimensional measuring equipment are adopted for detecting the pavement construction depth, a construction depth calculation model is not explicitly detected, the sand spreading method in the specification is to spread sand, the sand spreading range is the construction depth detection range, the sand volume and sand spreading area ratio is taken as the construction depth, the existing methods for detecting the construction depth detection range by the laser, image and three-dimensional measuring equipment cannot identify the boundary of the construction depth detection range, the void volume of the filling surface in the detection range cannot be accurately calculated, the construction depth detection result cannot directly and objectively reflect the specification limit value requirement, the accuracy of the detection value result cannot be scientifically judged, and the application range is limited. Therefore, it is highly desirable to provide a method for detecting the construction depth, which provides a scientific construction depth calculation model based on an informationized detection means, can practically reflect a limit value required by a method in a standard, and further can scientifically, objectively and effectively evaluate the construction quality of a pavement.
Disclosure of Invention
Aiming at the problems that the prior laser, image and three-dimensional measuring equipment is involved in boundary recognition and the loss of a gap volume calculation model of a filling surface in the range in the construction depth detection process, the construction depth detection method is difficult to practically reflect the actual use effect, the surface gap recognition and gap volume calculation model in a certain range is inverted through the image data and three-dimensional data splicing technology, the construction depth calculation method in combination with the specification is used for determining the area formed by filling the volume on the surface of the pavement through the setting of a gap volume threshold of the filling surface, so that the construction depth is calculated, and the pavement is evaluated scientifically, objectively and effectively.
The technical problems to be solved by the invention are realized by adopting the following technical scheme:
The method for detecting the construction depth of the large-gap asphalt pavement based on image processing comprises the following steps:
(1) On a large-gap asphalt pavement, determining a 3D scanning station and a control point position layout scheme;
(2) Measuring the coordinates and the elevation of the measuring station and the control point by using a total station to obtain the space coordinates of the measuring station and the control point as known coordinates;
(3) The 3D scanner is arranged according to the measuring stations, and the space information of the road surface is acquired, so that the road surface acquisition coordinate point cloud data are obtained.
(4) And according to the control points, the coordinates acquired by different measuring stations are spliced with point cloud data. According to the relation between the known coordinates and the acquisition coordinates of the measuring station and the control point, coordinate conversion is carried out on the acquisition coordinate point cloud data of the 3D scanner, the coordinate conversion is carried out to an actual coordinate, and the calculation is carried out according to the following formula:
；
wherein: -rotating the matrix; -translating the matrix; 、、 -transformed coordinates; 、、 -pre-conversion coordinates;
(5) And arranging a structural depth detection point and at least 2 control points on the large-gap asphalt pavement, and carrying out image acquisition in an image range by taking the actual coordinates of the structural depth detection point as the center, intercepting the image with the size of a square with the structural depth as the center and the side length of 300-400 mm, and carrying out filtering and histogram equalization treatment on the image.
(6) And according to the actual coordinates of the construction depth detection point and the control point, carrying out data splicing on the point cloud data and the acquired picture data, and carrying out uniform space segmentation on the spliced data.
(7) Taking the actual coordinates of the depth detection points as circle centers, the radius of the circles is 20mm, extracting each gap edge of the asphalt pavement in the images within the circle range, and carrying out curved surface reconstruction in the segmentation units according to the point cloud data and picture data splicing result by adopting a BP neural network modelExtracting each gap edge coordinate set of asphalt pavement in image rangeThe volume of each void was calculated according to the following formula:
；
；
；
Wherein: -individual void volume, mm 3; -number of individual void edge coordinates;
-single void edge average vertical coordinate, m; -the voids occupy the number of cells; -dividing the void volume within the cell, mm 3;
(8) Taking the actual coordinates of the constructional depth points as circle centers, counting the total volume of all the gaps in the circular range, wherein the radius of the circle is 20mm, gradually increasing the radius of the circle center according to a certain step distance, counting the total volume of all the gaps in the circular range, recording the radius of the circle at the moment when the total volume of the gaps in the circular range exceeds 25cm 3, and stopping increasing the step distance of the radius of the circle.
(9) The depth of formation of the inspection point is calculated from the recorded radius of the circle when the void volume in the circle exceeds 25cm 3 and the recorded radius of the circle when the void volume in the circle exceeds 25cm 3, according to the following formula:
；
Wherein: -construction depth, mm; Radius at which the circular stride stops increasing.
Preferably, the void ratio of the large-void asphalt pavement is 15-25%, the radius of the circle center is gradually increased according to the step distance of 2-5 mm, and the dividing size of the unit cells is 0.05-0.2 mm.
The beneficial technical effects of the invention are as follows:
(1) The invention discloses a method for detecting the construction depth of a large-gap asphalt pavement based on image processing, which is characterized in that the edge of a road surface gap is identified based on three-dimensional point cloud data and an image processing technology, a calculation model of the surface gap volume of the large-gap asphalt pavement is established, the area formed in the road surface after the surface gap is filled by a volume threshold is determined through setting the volume threshold according to the construction depth calculation method in a standard, and then the construction depth is calculated according to the method in the standard.
(2) The invention provides a large-gap asphalt pavement structure depth detection method, which is established by a data model based on a point cloud data and image processing combined technical method, is based on a standard structure depth calculation theory, has high accuracy, can judge pavement structure depth distribution condition in real time, and is convenient to improve in time in the construction process.
(3) The invention relates to a method for detecting the construction depth of a large-gap asphalt pavement based on image processing, which is a nondestructive, rapid and pollution-free detection means, avoids road surface pollution caused by detection means such as a sanding method, adopts an image processing technology and an informatization processing technology, greatly improves the efficiency of detection and evaluation, and can provide theoretical basis for pavement disease cause analysis, construction condition assessment, engineering acceptance and the like as a result of calculation, and the technical content is simple to operate and easy to realize.
Detailed Description
The invention is further described in the following with reference to specific embodiments in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
The invention provides a method for detecting the construction depth of a large-gap asphalt pavement based on image processing.
All the raw materials in the present invention are not particularly limited in their sources, and may be purchased on the market or prepared according to conventional methods well known to those skilled in the art.
The purity of all the raw materials in the present invention is not particularly limited, and the present invention preferably employs a conventional purity used in the field of analytical purity or composite materials.
Example 1: the method comprises the following main steps of:
And determining a 3D scanning measuring station and a control point position layout scheme according to the road surface condition and the detection range, and selecting 3 measuring stations and 4 control points.
And measuring the coordinates and the elevation of the measuring station and the control point by using a total station. Measuring coordinates as。
Laying according to 3 measuring stations by using a 3D scanner, and acquiring the space information of the road surface so as to obtain road surface acquisition coordinate point cloud data （x10,y10,z10）......（x1n,y1n,z1n）,（x20,y20,z20）......（x2n,y2n,z2n）,（x30,y30,z30）......（x3n,y3n,z3n）.
And according to the control points, the coordinates acquired by different measuring stations are subjected to point cloud data splicing, and the spliced point cloud coordinates are (X Old one 0,Y Old one 0,Z Old one 0）......（X Old one m,Y Old one m,Z Old one m).
Calculating a rotation matrix and a translation matrix by Maltlab software from a station to be measured of the total station, known coordinates of a control point and the station acquired by the 3D scanner;
And acquiring coordinate point cloud data of the 3D scanner, obtaining a rotation matrix R and a translation matrix B according to the data, and carrying out coordinate conversion on the acquired coordinates of the 3D scanner, wherein the converted coordinates are (X New type 0,Y New type 0,Z New type 0）......（X New type m,Y New type m,Z New type m).
On the large-gap asphalt pavement, 1 control point and 2 control points of a structural depth detection point are distributed and are positioned in an image range, image acquisition is carried out by taking the actual coordinates of the structural depth detection point as the center, the image is intercepted, the center of the intercepted image is the structural depth detection point, the side length of the intercepted image is a square with 400mm, and filtering and histogram equalization processing are carried out on the image.
And according to the actual coordinates of the construction depth detection point and the control point, carrying out data splicing on the point cloud data and the acquired picture data, and carrying out uniform space segmentation on the spliced data, wherein the segmentation size is 0.1mm.
Taking the actual coordinates of the depth detection points as circle centers, the radius of the circles is 20mm, and extracting the edge coordinate set of each gap of the asphalt pavement in the image in the circular range according to the splicing result of the point cloud data and the picture dataAdopts BP neural network model to reconstruct curved surface in the dividing unitThe number of cells occupied by the voids was 30, and the volume of each void was calculated according to the following formula:
；
；
。
the calculation results are as follows:
TABLE 1 Single void volume calculation results
The volumes of all voids within the image were calculated as follows:
Table 2 results of all void volume calculations over the image range
Taking the actual coordinates of the construction depth points as circle centers, the radius of the circle is 20mm, counting the total volume of all gaps in the circular range, gradually increasing the radius of the circle centers according to the step distance of 3mm, counting the total volume of all gaps in the circular range, and repeating the above operations, wherein the result is as follows:
TABLE 3 calculation of all void volume calculations over circular ranges of different radii
When the total volume of the voids in the circular range exceeds 25cm 3, the radius of the circle is 80mm, and the increase of the radius of the circle is stopped.
The depth of the formation of the detection points is calculated as follows:
。
Control example: according to the method specified in the on-site detection procedure of highway subgrade and road surface, the construction depth of the detection point is measured according to a manual sanding method, and the result is that The error of the detection result compared with the detection method is only 2.4%, and the surface detection method has good applicability.
The foregoing has outlined and described the basic principles, main features and features of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (5)
1. The method for detecting the construction depth of the large-gap asphalt pavement based on image processing is characterized by comprising the following steps of:
(1) On a large-gap asphalt pavement, determining a 3D scanning station and a control point position layout scheme;
(2) Measuring the coordinates and the elevation of the measuring station and the control point by using a total station to obtain the space coordinates of the measuring station and the control point as known coordinates;
(3) The 3D scanner is arranged according to the measuring stations, and acquires the space information of the road surface, so that the road surface acquisition coordinate point cloud data are obtained;
(4) According to the control points, the coordinates acquired by different measuring stations are spliced with point cloud data; according to the relation between the known coordinates and the acquisition coordinates of the measuring station and the control point, coordinate conversion is carried out on the acquisition coordinate point cloud data of the 3D scanner, and the coordinate conversion is converted into actual coordinates;
(5) Arranging a construction depth detection point and a control point on the large-gap asphalt pavement, collecting an image by taking the actual coordinates of the construction depth detection point as the center, intercepting the image, and carrying out filtering and histogram equalization treatment on the image;
(6) According to the actual coordinates of the construction depth detection point and the control point, carrying out data splicing on the point cloud data and the acquired picture data, and carrying out uniform space segmentation on the spliced data;
(7) Taking the actual coordinates of the depth detection points as circle centers and the radius of the circles as 20mm, extracting the edge of each gap of the asphalt pavement in the image in the circular range, reconstructing a curved surface in a segmentation unit according to the splicing result of the point cloud data and the picture data, calculating the volume of each gap in the circular range, and counting the total volume of all gaps in the circular range;
(8) Gradually increasing the circular radius according to a certain step distance, counting the total volume of all gaps in the circular range, recording the circular radius when the total volume of the gaps in the circular range exceeds 25cm 3, and stopping increasing the step distance of the circular radius;
(9) From the recorded radius of the circle when the void volume in the circle exceeds 25cm 3, the depth of formation of the inspection point is calculated according to the following formula:
；
wherein: -construction depth, mm; Radius at which the circular stride stops increasing.
2. The image processing-based large-void asphalt pavement construction depth detection method according to claim 1, wherein: the coordinate conversion is carried out on the coordinate point cloud data acquired by the 3D scanner, the coordinate point cloud data are converted into actual coordinates, and the coordinate point cloud data are calculated according to the following formula:
；
wherein: -rotating the matrix; -translating the matrix; 、、 -transformed coordinates; 、、 -pre-conversion coordinates.
3. The image processing-based large-void asphalt pavement construction depth detection method according to claim 2, wherein: the method comprises the steps of constructing depth detection points and control points, wherein the number of the control points is not less than 2 and is located in an image range, and the image is intercepted, wherein the intercepted size is a square with the depth detection points as the center and the side length of 300-400 mm.
4. The image processing-based large-void asphalt pavement construction depth detection method according to claim 3, wherein: the splicing data is subjected to uniform space segmentation, a BP neural network model is adopted, and curved surface reconstruction is carried out in a segmentation unitExtracting each gap edge coordinate set of asphalt pavement in image rangeThe volume of each void was calculated according to the following formula:
；
；
；
wherein: -individual void volume, mm 3; -number of individual void edge coordinates;
-single void edge average vertical coordinate, m; -the voids occupy the number of cells; -dividing the void volume within the cell, mm 3.
5. The image processing-based large-void asphalt pavement construction depth detection method according to claim 4, wherein: the large-gap asphalt pavement has a void ratio of 15-25%, the circular radius is gradually increased according to a step distance of 2-5 mm, and the cell division size is 0.05-0.2 mm.
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| CN117647220A (en) * | 2024-01-25 | 2024-03-05 | 安徽省交通规划设计研究总院股份有限公司 | Asphalt pavement subsidence treatment method based on laser point cloud data |
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| CN107883891B (en) * | 2017-12-27 | 2020-03-17 | 长安大学 | Method for identifying multidimensional characteristic indexes of asphalt pavement upheaval |
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| CN117647220A (en) * | 2024-01-25 | 2024-03-05 | 安徽省交通规划设计研究总院股份有限公司 | Asphalt pavement subsidence treatment method based on laser point cloud data |
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