An asphalt road pavement asphalt layer thickness detection device and method

Abstract

The present application discloses a kind of asphalt road pavement asphalt layer thickness detection device and method, it is related to road engineering detection technical field, the present application includes data acquisition module, data analysis module and data processing module;Millimeter wave radar probe and line laser displacement sensor integrated on the detection vehicle are used to target detection area synchronous scanning, dynamic inclination compensation is generated to the laser elevation data in combination with attitude information to generate corrected point cloud;Based on space-time synchronization information, the corrected point cloud is spatially registered with the radar scan points, and the road surface elevation value at the vertical projection position of the radar points is extracted;A continuous dynamic datum plane model is constructed by spatial interpolation method, the distance from the probe to the target interface is calculated by combining the radar signal round-trip time difference and the asphalt dielectric constant, and finally the asphalt layer thickness value and spatial distribution map are output.

Description

Technical Field

[0001] This invention relates to the field of road engineering testing technology, specifically to a device and method for detecting the thickness of asphalt layer in asphalt road pavement. Background Technology

[0002] The thickness of the asphalt layer on asphalt roads is a core indicator of road quality, directly affecting pavement durability and driving safety. Traditional testing mainly relies on core drilling, which requires damaging the pavement structure and is inefficient. Single-point testing is time-consuming and cannot meet the needs of long-distance continuous testing on highways and urban arterial roads. With the development of non-destructive testing technologies, ground-penetrating radar and laser scanning methods are gradually being applied, but bottlenecks such as insufficient accuracy and weak anti-interference still exist. There is an urgent need for new testing solutions that are high-precision, high-efficiency, and fully continuous.

[0003] Traditional methods for measuring asphalt pavement thickness have limitations. Ground-penetrating radar (GPR) can detect underground interfaces but is affected by surface undulations, resulting in thickness calculation errors exceeding ±3cm. Laser displacement sensors can accurately obtain road surface elevation but cannot penetrate the material, making independent thickness measurement impossible. In multi-sensor fusion solutions, the spatial positions of radar detection points and laser measurement points are offset due to differences in installation location and vehicle vibrations. Traditional registration methods fail to eliminate this error, leading to thickness calculation deviations when the road surface is tilted. The complex morphology of the asphalt layer's underside means that current technologies rely on discrete point interpolation, failing to establish a continuous dynamic reference surface model and thus unable to reflect the overall slope and undulations of the underside, resulting in inconsistent thickness data over long distances.

[0004] To address the aforementioned pain points, this invention proposes a multi-source data collaboration mechanism involving millimeter-wave radar, laser, GPS / IMU, and vertical projection mapping, dynamic tilt compensation, and precise spatial registration to overcome the challenges of spatial misalignment and benchmark modeling. Summary of the Invention

[0005] The purpose of this invention is to provide a device and method for detecting the thickness of asphalt layer in asphalt road pavement, which solves the problems existing in the background art.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The first aspect of the present invention provides an asphalt layer thickness detection device for asphalt road pavement, comprising: a data acquisition module: using a millimeter-wave radar probe and a line laser displacement sensor integrated on the detection vehicle to perform synchronous scanning and measurement of the target detection area, and using a GPS / IMU combined positioning system to record the position, speed and attitude information of the detection vehicle in real time; and acquiring the original radar echo sequence, the original laser elevation data and the spatiotemporally synchronized position and attitude information.

[0007] Data analysis module: used to preprocess the original radar echo sequence and identify strong reflection points at the interface between corresponding asphalt layers; combined with the attitude information of the inspection vehicle, it generates corrected laser elevation point cloud data; based on the spatiotemporally synchronized position and attitude information, it performs spatially precise registration between the corrected laser elevation point cloud data and the radar scanning point position.

[0008] Preferably, the method for identifying strong reflection points at the interface between corresponding asphalt layers is as follows: the asphalt layer includes an asphalt top layer, an asphalt middle layer, and an asphalt bottom layer.

[0009] The interlayer interfaces of the asphalt layers include the interface between the asphalt top layer and the asphalt intermediate layer, the interface between the asphalt intermediate layer and the asphalt bottom layer, and the interface between the asphalt bottom layer and the base layer.

[0010] Based on the preprocessed radar echo sequence, the amplitude feature values ​​of the reflected echo signal at each time moment are extracted.

[0011] The radar echo sequence is the raw data set of reflected echo signals from various interfaces inside the asphalt layer, continuously received after the millimeter-wave radar probe emits a broadband millimeter-wave signal.

[0012] The preset amplitude threshold is obtained from the database, and signal points whose amplitude characteristic value of the reflected echo signal exceeds the threshold are initially identified as candidate strong reflection points.

[0013] The preset dielectric constant parameters of the asphalt layer material are obtained from the database. For candidate strong reflection points, the dielectric constant difference between the materials on both sides of the interface between the asphalt lower layer and the base layer is calculated based on the preset dielectric constant parameters of the asphalt layer material. Candidate strong reflection points whose dielectric constant difference is within the allowable dielectric constant range are retained as strong reflection points.

[0014] Preferably, the method for generating the corrected laser elevation point cloud data is as follows: based on the attitude information of the detection vehicle, dynamic tilt compensation is performed on the original laser elevation data.

[0015] Based on the corrected elevation value of each laser measurement point after dynamic tilt compensation, and combined with the vehicle location information obtained from the GPS / IMU integrated positioning system, the latitude and longitude are converted into coordinates in a unified plane coordinate system, and three-dimensional spatial coordinates are assigned to each corrected elevation value. ,in , For planar coordinates, To correct the elevation values, all three-dimensional spatial coordinate points are compiled to form corrected laser elevation point cloud data.

[0016] Preferably, the method for dynamically tilting the raw laser elevation data based on the attitude information of the detection vehicle is as follows: the attitude information includes pitch angle and roll angle; the raw laser elevation data is the raw elevation value of each surface point of the road surface directly output by the line laser displacement sensor during the scanning process.

[0017] The pitch angle of the test vehicle at each measurement moment is read in real time from the GPS / IMU combined positioning system. and roll angle The pitch angle is the tilt angle of the detection vehicle about its lateral axis, and the roll angle is the tilt angle of the detection vehicle about its longitudinal axis; and raw laser elevation data is acquired simultaneously. .

[0018] Two-dimensional tilt compensation is performed on the original laser elevation data: first based on the pitch angle. Longitudinal compensation is performed, and the calculation formula is as follows: Based on the roll angle Lateral compensation is performed, and the calculation formula is as follows: The corrected elevation value is obtained. .

[0019] Preferably, the method for precisely registering the corrected laser elevation point cloud data with the radar scanning point position is as follows: the radar scanning point is a strong reflection point at the interface between the identified asphalt layers.

[0020] The GPS coordinates of the corrected laser elevation point cloud and the GPS coordinates of the radar scanning points are converted into the same plane coordinate system. Based on the spatiotemporally synchronized position and attitude information, a one-to-one correspondence between the radar scanning points and the corrected laser elevation point cloud in the time dimension is established. The spatiotemporally synchronized position and attitude information refers to the position, speed, and attitude information of the detection vehicle recorded at the same timestamp through the GPS / IMU combined positioning system. Through translation and rotation coordinate transformation algorithms, the vertical projection point of each radar scanning point on the road surface is precisely aligned with the planar position of the corrected laser elevation point measured by the laser sensor.

[0021] Data processing module: For each radar scanning point, extract the corrected laser elevation value of its vertical projection point on the road surface from the registered laser point cloud; calculate the electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe; calculate the asphalt layer thickness; output the asphalt layer thickness value and its spatial location information for each scanning point; and generate a continuous thickness distribution map.

[0022] Preferably, the method for calculating the electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe is as follows: based on the extracted set of corrected laser elevation points at the road surface location, a continuous dynamic reference surface model is constructed using spatial interpolation.

[0023] The target interface is the interface between the asphalt sublayer and the base layer.

[0024] The round-trip time difference between the millimeter-wave radar signal and the target interface was obtained from the original radar echo sequence. Based on the preset average relative permittivity of asphalt material According to the formula for the speed of electromagnetic wave propagation Calculate the propagation speed, where The speed of light in a vacuum; the electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe. .

[0025] Preferably, the method for constructing a continuous dynamic reference surface model using spatial interpolation is as follows: taking the boundary of the target detection area as the interpolation range, wherein the target detection area is a continuous road segment where the asphalt layer thickness needs to be measured during the driving of the detection vehicle, the corrected laser elevation value of the road surface position is extracted from the registered laser point cloud; and using spatial interpolation, the elevation of the discrete point set is estimated to generate a continuous reference surface model covering the target detection area.

[0026] The spatial interpolation method is a method that estimates unknown point data through mathematical modeling based on finite known radar scan point data, transforming discretely distributed radar scan points into a continuous spatial surface, which is used to construct a reference surface model with spatial continuity.

[0027] Preferably, the method for calculating the asphalt layer thickness is as follows: obtaining the installation height of the millimeter-wave radar probe from a database. The installation height The asphalt layer thickness was pre-determined using high-precision measuring tools. .

[0028] Preferably, the method for generating a continuous thickness distribution map by outputting the asphalt layer thickness value and its spatial location information at each scanning point is as follows: The asphalt layer thickness at each scanning point is... With planar spatial coordinates Connection, formation Data set; the scanning points are discrete measurement points collected at preset intervals when the millimeter-wave radar probe scans the target detection area; when generating the thickness continuous distribution map, spatial interpolation is used to interpolate the discrete thickness values ​​into a continuous surface, and the thickness distribution is identified by color gradient.

[0029] The second aspect of the present invention provides a method for performing the asphalt layer thickness detection device for asphalt pavement of the present invention, comprising: Step 1. Data acquisition: synchronously scanning and measuring the target detection area using a millimeter-wave radar probe and a line laser displacement sensor integrated on the detection vehicle, and recording the position, speed and attitude information of the detection vehicle in real time through a GPS / IMU combined positioning system; acquiring the original radar echo sequence, the original laser elevation data and the spatiotemporally synchronized position and attitude information.

[0030] Step 2. Data Analysis: Preprocess the original radar echo sequence to identify strong reflection points at the interfaces between corresponding asphalt layers; combine the attitude information of the inspection vehicle to generate corrected laser elevation point cloud data; based on the spatiotemporally synchronized position and attitude information, perform spatially precise registration between the corrected laser elevation point cloud data and the radar scanning point positions.

[0031] Step 3. Data Processing: For each radar scanning point, extract the corrected laser elevation value of its vertical projection point on the road surface from the registered laser point cloud; calculate the electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe; calculate the asphalt layer thickness; output the asphalt layer thickness value and its spatial location information for each scanning point; and generate a continuous thickness distribution map.

[0032] The beneficial effects of the present invention are as follows: (1) The first part of the present invention: by using vertical projection mapping and dynamic reference surface modeling technology, the spatial position is accurately matched at the centimeter level, the influence of spatial misalignment error is eliminated, and the thickness deviation caused by road surface tilt and sensor misalignment in the traditional method is solved.

[0033] (2) The second part of the invention: Integrating millimeter-wave radar, laser displacement and high-precision positioning system to construct millisecond-level spatiotemporal closed loop. Dynamic tilt compensation and dielectric characteristic threshold automatically lock the target interface, realizing high-speed continuous detection on the vehicle and improving efficiency.

[0034] (3) The third part of the invention: an embedded intelligent parameter library and adaptive algorithm to eliminate the need for manual intervention. The thickness distribution map visually displays road conditions using gradient chromatograms, supporting steep roads and urban traffic environments, and providing a full-scenario solution for road maintenance. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0037] Figure 2 This is a schematic diagram of the implementation steps of the method of the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] Reference Figure 1 As shown, the first aspect of the present invention provides an asphalt layer thickness detection device for asphalt road pavement, comprising: a data acquisition module, a data analysis module, and a data processing module.

[0040] It should be noted that the data acquisition module is connected to the data analysis module, the data analysis module is connected to the data processing module, and the database is connected to the data acquisition module, the data analysis module, and the data processing module.

[0041] It should also be noted that the database is used to store preset amplitude thresholds, preset dielectric constant parameters of asphalt layer materials, and the installation height of millimeter-wave radar probes. .

[0042] The data acquisition module uses a millimeter-wave radar probe and a line laser displacement sensor integrated on the detection vehicle to simultaneously scan and measure the target detection area, and records the position, speed and attitude information of the detection vehicle in real time through a GPS / IMU combined positioning system; it also collects the original radar echo sequence, the original laser elevation data and the spatiotemporally synchronized position and attitude information.

[0043] It should be noted that the millimeter-wave radar probe emits broadband millimeter-wave signals along the detection direction and receives reflected echo signals from the interface between asphalt layers; the line laser displacement sensor simultaneously measures the microscopic elevation profile information corresponding to the vertical projection point of the millimeter-wave radar probe on the road surface.

[0044] The data analysis module is used to preprocess the original radar echo sequence, identify strong reflection points at the interface between corresponding asphalt layers, generate corrected laser elevation point cloud data by combining the attitude information of the detection vehicle, and perform spatially accurate registration of the corrected laser elevation point cloud data with the radar scanning point position based on the spatiotemporally synchronized position and attitude information.

[0045] In one specific embodiment, the method for identifying strong reflection points at the interface between corresponding asphalt layers is as follows: the asphalt layer includes an asphalt top layer, an asphalt middle layer, and an asphalt bottom layer.

[0046] For example, based on the parameters obtained in the early stage of asphalt paving, the thickness of the asphalt surface layer ranges from 4 to 5 cm, the thickness of the intermediate layer ranges from 6 to 8 cm, and the thickness of the bottom layer ranges from 7 to 10 cm.

[0047] The interlayer interfaces of the asphalt layers include the interface between the asphalt top layer and the asphalt intermediate layer, the interface between the asphalt intermediate layer and the asphalt bottom layer, and the interface between the asphalt bottom layer and the base layer.

[0048] It should be noted that the interface between the asphalt subbase and the base course is the main target interface for testing. The base course is usually made of cement-stabilized crushed stone or similar materials.

[0049] Based on the preprocessed radar echo sequence, the amplitude feature values ​​of the reflected echo signal at each time moment are extracted;

[0050] The radar echo sequence is the raw data set of reflected echo signals from various interfaces inside the asphalt layer, which are continuously received after the millimeter-wave radar probe emits a broadband millimeter-wave signal.

[0051] It should be noted that the radar echo sequence is time-based and includes information such as the amplitude and phase of the reflected echo signal at each moment;

[0052] The preset amplitude threshold is obtained from the database, and signal points whose amplitude characteristic value of the reflected echo signal exceeds the threshold are initially identified as candidate strong reflection points.

[0053] It should be noted that the preset amplitude threshold is 3-5 times the average amplitude; for example, in this embodiment, 4 times the average amplitude is used as the preset amplitude threshold.

[0054] The preset dielectric constant parameters of the asphalt layer material are obtained from the database. For candidate strong reflection points, the dielectric constant difference between the materials on both sides of the interface between the asphalt lower layer and the base layer is calculated based on the preset dielectric constant parameters of the asphalt layer material. Candidate strong reflection points whose dielectric constant difference is within the allowable dielectric constant range are retained as strong reflection points.

[0055] For example, in this embodiment, the dielectric constant range is allowed to be ≥2, because the difference in dielectric constant is the core reason for strong reflection, and the reflected signal characteristics are significant when the difference in dielectric constant is ≥2.

[0056] In one specific embodiment, the method for generating corrected laser elevation point cloud data is as follows: based on the attitude information of the detection vehicle, dynamic tilt compensation is performed on the original laser elevation data;

[0057] Based on the corrected elevation value of each laser measurement point after dynamic tilt compensation, and combined with the vehicle location information obtained from the GPS / IMU integrated positioning system, the latitude and longitude are converted into coordinates in a unified plane coordinate system, and three-dimensional spatial coordinates are assigned to each corrected elevation value. ,in , For planar coordinates, To correct the elevation values, all three-dimensional spatial coordinate points are compiled to form corrected laser elevation point cloud data.

[0058] It should be noted that the corrected laser elevation point cloud data consists of a massive amount of discrete three-dimensional points. The composition and density of the points are related to the scanning frequency of the laser sensor and the speed of the detection vehicle. For example, 10-50 points per square meter can accurately reflect the micro-undulations of the road surface. The value represents the true elevation after eliminating the effects of the vehicle's tilt. , The values ​​are planar coordinates in the same coordinate system as the radar scanning points to ensure the accuracy of spatial location.

[0059] In one specific embodiment, the method for dynamically tilting the raw laser elevation data based on the attitude information of the detection vehicle is as follows: the attitude information includes pitch angle and roll angle; the raw laser elevation data is the raw elevation value of each surface point of the road surface directly output by the line laser displacement sensor during the scanning process.

[0060] It should be noted that the original elevation value is the measurement value of the linear laser displacement sensor along the measurement direction, which can reflect the micro-undulations of the road surface. However, it is affected by the change of the vehicle's posture. For example, when the vehicle is tilted, the original elevation value is not the true elevation perpendicular to the ground and there is a projection error.

[0061] The pitch angle of the test vehicle at each measurement moment is read in real time from the GPS / IMU combined positioning system. and roll angle The pitch angle is the tilt angle of the detection vehicle about its lateral axis, and the roll angle is the tilt angle of the detection vehicle about its longitudinal axis; and raw laser elevation data is acquired simultaneously. ;

[0062] Two-dimensional tilt compensation is performed on the original laser elevation data: first based on the pitch angle. Longitudinal compensation is performed, and the calculation formula is as follows: Based on the roll angle Lateral compensation is performed, and the calculation formula is as follows: The corrected elevation value is obtained. .

[0063] In one specific embodiment, the method for precisely registering the corrected laser elevation point cloud data with the radar scanning point position is as follows: the radar scanning point is a strong reflection point at the interface between asphalt layers identified.

[0064] The GPS coordinates of the corrected laser elevation point cloud and the GPS coordinates of the radar scanning points are converted into the same plane coordinate system. Based on the spatiotemporally synchronized position and attitude information, a one-to-one correspondence between the radar scanning points and the corrected laser elevation point cloud in the time dimension is established. The spatiotemporally synchronized position and attitude information refers to the position, speed, and attitude information of the detection vehicle recorded at the same timestamp through the GPS / IMU combined positioning system. Through translation and rotation coordinate transformation algorithms, the vertical projection point of each radar scanning point on the road surface is precisely aligned with the planar position of the corrected laser elevation point measured by the laser sensor.

[0065] It should be noted that the alignment accuracy is controlled within ±2cm;

[0066] It should be noted that the one-to-one correspondence is the same as the radar reflection point and the laser elevation point collected at the same time.

[0067] It should be noted that the radar scanning point corresponds to a certain interlayer interface within the asphalt layer, while the corrected laser elevation point corresponds to the road surface. The corrected laser elevation point cloud data reflects the three-dimensional position of the road surface. The radar scanning points, or strong reflection points, reflect the interfaces between underground layers, such as the three-dimensional position of the bottom surface of the asphalt layer. The two belong to different spatial levels, but they are established on the same plane coordinate system. The following diagram illustrates the relationship between road surface elevation and subsurface depth, which will inform subsequent calculations of asphalt layer thickness. Provides a spatial reference;

[0068] It should be noted that the asphalt layer thickness is the vertical distance from the road surface to the underground target interface. If the two are not precisely aligned in space—for example, if the radar scanning point corresponds to point A on the road surface, but the laser elevation value is taken from point B—it will directly lead to spatial misalignment errors in the thickness calculation. For instance, a 10-centimeter planar deviation could introduce several centimeters of thickness error when the road surface is inclined. After registration, this error can be controlled to the centimeter level, meeting the thickness accuracy requirements of engineering inspections, and typically meeting... The requirement is 2 centimeters.

[0069] The data processing module: for each radar scanning point, extracts the corrected laser elevation value of its vertical projection point on the road surface from the registered laser point cloud; calculates the electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe; calculates the asphalt layer thickness; outputs the asphalt layer thickness value and its spatial location information for each scanning point; and generates a continuous thickness distribution map.

[0070] It should be noted that the vertical projection point refers to the point projected along the geodetic coordinate system, with the radar scan point's plane coordinates as the reference. The negative axis is projected onto the point formed by the road surface.

[0071] In one specific embodiment, the method for calculating the electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe is as follows: based on the extracted set of corrected laser elevation points of the road surface location, a continuous dynamic reference surface model is constructed by spatial interpolation.

[0072] The target interface is the interface between the asphalt subbase and the base course;

[0073] The round-trip time difference between the millimeter-wave radar signal and the target interface was obtained from the original radar echo sequence. Based on the preset average relative permittivity of asphalt material According to the formula for the speed of electromagnetic wave propagation Calculate the propagation speed, where The speed of light in a vacuum; the electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe. .

[0074] It should be noted that the radar transmits a signal every 0.1 milliseconds, and the received reflected echo signals are arranged in chronological order to form sequence data;

[0075] It should be noted that the average relative permittivity of asphalt materials The average relative permittivity of asphalt mixtures is typically determined in advance through experiments. ;speed of light in vacuum about ;

[0076] It should be noted that, due to The distance is the time difference between the electromagnetic wave traveling to and from the millimeter-wave radar probe and the target interface. Therefore, half of this time difference must be taken when calculating the one-way distance to ensure the accuracy of the distance value.

[0077] In one specific embodiment, the method for constructing a continuous dynamic reference surface model using spatial interpolation is as follows: taking the boundary of the target detection area as the interpolation range, wherein the target detection area is a continuous road segment where the asphalt layer thickness needs to be measured during the driving of the detection vehicle, the corrected laser elevation value of the road surface position is extracted from the registered laser point cloud; and using spatial interpolation, the elevation of the discrete point set is estimated to generate a continuous reference surface model covering the target detection area.

[0078] The spatial interpolation method is a method that estimates unknown point data through mathematical modeling based on finite known radar scan point data, transforming discretely distributed radar scan points into a continuous spatial surface, which is used to construct a reference surface model with spatial continuity.

[0079] It should be noted that the target detection area is usually a continuous road area, such as a section of highway or urban main road; its range can be determined according to the detection needs, such as a 1-kilometer road section or a specific defect area.

[0080] It should be noted that both the radar scan points and the corrected laser elevation values ​​are discretely distributed, with 10-20 points collected per meter, while the bottom surface of the asphalt layer is a continuous physical interface. Relying solely on discrete points to calculate the thickness would result in a lack of data support for the areas between adjacent points, failing to reflect the overall morphology of the bottom surface, such as slope and undulation. By constructing a continuous model, the gaps between discrete points can be filled, achieving a complete representation of the bottom surface morphology of the entire target detection area.

[0081] In one specific embodiment, the method for calculating the asphalt layer thickness is as follows: obtaining the installation height of the millimeter-wave radar probe from a database. The installation height The asphalt layer thickness was pre-determined using high-precision measuring tools. .

[0082] It should be noted that the physical meaning of asphalt layer thickness is the vertical distance from the road surface to the target interface, i.e., the interface between the asphalt subbase and the base course. This value is equal to the vertical distance from the millimeter-wave radar probe to the road surface. Electromagnetic wave propagation distance from millimeter-wave radar probe to target interface The difference is calculated in this way, which directly reflects the actual thickness of the asphalt layer.

[0083] In one specific embodiment, the method for outputting the asphalt layer thickness value and its spatial location information at each scanning point to generate a continuous thickness distribution map is as follows: The asphalt layer thickness at each scanning point is... With planar spatial coordinates Connection, formation Data set; the scanning points are discrete measurement points collected at preset intervals when the millimeter-wave radar probe scans the target detection area; when generating the thickness continuous distribution map, spatial interpolation is used to interpolate the discrete thickness values ​​into a continuous surface, and the thickness distribution is identified by color gradient.

[0084] It should be noted that the preset interval is every 0.1 meters;

[0085] It should be noted that each scanning point corresponds to a location inside the asphalt layer, including: planar spatial coordinates. This reflects the two-dimensional position of the point on the road surface; the target interface reflects the echo signal, providing raw data for calculating the distance to the millimeter-wave radar probe;

[0086] It should be noted that, to generate the thickness continuous distribution map, spatial interpolation is used. The discrete thickness values ​​in the dataset are interpolated to form a continuous surface covering the entire target detection area; a color contour map is constructed using color gradients as visual identifiers, where red indicates areas with thicker asphalt layers, blue indicates areas with thinner layers, and intermediate color levels correspond to medium thicknesses. The color transitions visually reflect the differences in the spatial distribution of thickness.

[0087] Reference Figure 2 As shown, the second aspect of the present invention provides a method for performing the asphalt layer thickness detection device for asphalt road pavement described in the present invention, comprising: step 1. data acquisition, step 2. data analysis, and step 3. data processing.

[0088] Step 1. Data Acquisition: The target detection area is synchronously scanned and measured using a millimeter-wave radar probe and a line laser displacement sensor integrated on the detection vehicle. The position, speed, and attitude information of the detection vehicle are recorded in real time through a GPS / IMU combined positioning system. The original radar echo sequence, original laser elevation data, and spatiotemporally synchronized position and attitude information are collected.

[0089] Step 2. Data Analysis: Preprocess the original radar echo sequence to identify strong reflection points at the interfaces between corresponding asphalt layers; combine the attitude information of the inspection vehicle to generate corrected laser elevation point cloud data; based on the spatiotemporally synchronized position and attitude information, perform precise spatial registration between the corrected laser elevation point cloud data and the radar scanning point positions.

[0090] Step 3. Data Processing: For each radar scanning point, extract the corrected laser elevation value of its vertical projection point on the road surface from the registered laser point cloud; calculate the electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe; calculate the asphalt layer thickness; output the asphalt layer thickness value and its spatial location information for each scanning point; and generate a continuous thickness distribution map.

[0091] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A device for detecting the thickness of asphalt layer in asphalt road pavement, characterized in that, include: Data acquisition module: It uses a millimeter-wave radar probe and a line laser displacement sensor integrated on the detection vehicle to simultaneously scan and measure the target detection area, and records the position, speed and attitude information of the detection vehicle in real time through a GPS / IMU combined positioning system; it collects raw radar echo sequences, raw laser elevation data and spatiotemporally synchronized position and attitude information; Data analysis module: used to preprocess the original radar echo sequence and identify strong reflection points at the interface between corresponding asphalt layers; combined with the attitude information of the inspection vehicle, it generates corrected laser elevation point cloud data; based on the spatiotemporally synchronized position and attitude information, it performs spatially precise registration between the corrected laser elevation point cloud data and the radar scanning point position. Data processing module: For each radar scanning point, extract the corrected laser elevation value of its vertical projection point on the road surface from the registered laser point cloud; calculate the electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe; calculate the asphalt layer thickness; output the asphalt layer thickness value and its spatial location information for each scanning point; and generate a continuous thickness distribution map.

2. The asphalt layer thickness detection device for asphalt road pavement according to claim 1, characterized in that, The specific method for identifying the strong reflection points at the interlayer interface of the corresponding asphalt layer is as follows: The asphalt layer comprises an asphalt top layer, an asphalt intermediate layer, and an asphalt bottom layer; The interlayer interfaces of the asphalt layers include the interface between the asphalt top layer and the asphalt intermediate layer, the interface between the asphalt intermediate layer and the asphalt bottom layer, and the interface between the asphalt bottom layer and the base layer. Based on the preprocessed radar echo sequence, the amplitude feature values ​​of the reflected echo signal at each time moment are extracted; The radar echo sequence is the raw data set of reflected echo signals from various interfaces inside the asphalt layer, which are continuously received after the millimeter-wave radar probe emits a broadband millimeter-wave signal. The preset amplitude threshold is obtained from the database, and signal points whose amplitude characteristic value of the reflected echo signal exceeds the threshold are initially identified as candidate strong reflection points. The preset dielectric constant parameters of the asphalt layer material are obtained from the database. For candidate strong reflection points, the dielectric constant difference between the materials on both sides of the interface between the asphalt lower layer and the base layer is calculated based on the preset dielectric constant parameters of the asphalt layer material. Candidate strong reflection points whose dielectric constant difference is within the allowable dielectric constant range are retained as strong reflection points.

3. The asphalt layer thickness detection device for asphalt road pavement according to claim 1, characterized in that, The specific method for generating the corrected laser elevation point cloud data is as follows: Based on the attitude information of the detection vehicle, dynamic tilt compensation is performed on the original laser elevation data; Based on the corrected elevation value of each laser measurement point after dynamic tilt compensation, and combined with the vehicle location information obtained from the GPS / IMU integrated positioning system, the latitude and longitude are converted into coordinates in a unified plane coordinate system, and three-dimensional spatial coordinates are assigned to each corrected elevation value. ,in , For planar coordinates, To correct the elevation values, all three-dimensional spatial coordinate points are compiled to form corrected laser elevation point cloud data.

4. The asphalt layer thickness detection device for asphalt road pavement according to claim 3, characterized in that, The method for dynamically tilting the raw laser elevation data based on the attitude information of the detection vehicle is as follows: The attitude information includes pitch angle and roll angle; the raw laser elevation data is the raw elevation value of each surface point of the road surface directly output by the line laser displacement sensor during the scanning process. The pitch angle of the test vehicle at each measurement moment is read in real time from the GPS / IMU combined positioning system. and roll angle The pitch angle is the tilt angle of the detection vehicle about its lateral axis, and the roll angle is the tilt angle of the detection vehicle about its longitudinal axis; and raw laser elevation data is acquired simultaneously. ; Two-dimensional tilt compensation is performed on the original laser elevation data: first based on the pitch angle. Longitudinal compensation is performed, and the calculation formula is as follows: Based on the roll angle Lateral compensation is performed, and the calculation formula is as follows: The corrected elevation value is obtained. .

5. The asphalt layer thickness detection device for asphalt road pavement according to claim 1, characterized in that, The specific method for precisely registering the corrected laser elevation point cloud data with the radar scan point positions is as follows: The radar scanning points are the strong reflection points at the interfaces between asphalt layers that have been identified. The GPS coordinates of the corrected laser elevation point cloud and the GPS coordinates of the radar scan points are converted into the same plane coordinate system. Based on the spatiotemporally synchronized position and attitude information, a one-to-one correspondence between the radar scan points and the corrected laser elevation point cloud in the time dimension is established. The spatiotemporally synchronized position and attitude information refers to the position, speed, and attitude information of the detection vehicle recorded at the same timestamp through the GPS / IMU combined positioning system. Through translation and rotation coordinate transformation algorithms, the vertical projection point of each radar scan point on the road surface is precisely aligned with the plane position of the corrected laser elevation point measured by the laser sensor.

6. The asphalt layer thickness detection device for asphalt road pavement according to claim 1, characterized in that, The specific method for calculating the electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe is as follows: Based on the extracted corrected laser elevation point set of the road surface location, a continuous dynamic reference surface model is constructed by spatial interpolation. The target interface is the interface between the asphalt subbase and the base course; The round-trip time difference between the millimeter-wave radar signal and the target interface was obtained from the original radar echo sequence. Based on the preset average relative permittivity of asphalt material According to the formula for the speed of electromagnetic wave propagation Calculate the propagation speed, where It is the speed of light in a vacuum. Electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe .

7. The asphalt layer thickness detection device for asphalt road pavement according to claim 6, characterized in that, The specific method for constructing a continuous dynamic reference surface model using spatial interpolation is as follows: Using the boundary of the target detection area as the interpolation range, the target detection area is a continuous road segment where the asphalt layer thickness needs to be measured during the vehicle's operation. The corrected laser elevation value of the road surface position is extracted from the registered laser point cloud. The spatial interpolation method is used to estimate the elevation of the discrete point set and generate a continuous reference surface model covering the target detection area. The spatial interpolation method is a method that estimates unknown point data through mathematical modeling based on finite known radar scan point data, transforming discretely distributed radar scan points into a continuous spatial surface, which is used to construct a reference surface model with spatial continuity.

8. The asphalt layer thickness detection device for asphalt road pavement according to claim 6, characterized in that, The specific method for calculating the thickness of the asphalt layer is as follows: Obtain the installation height of the millimeter-wave radar probe from the database. The installation height The asphalt layer thickness was pre-determined using high-precision measuring tools. .

9. The asphalt layer thickness detection device for asphalt road pavement according to claim 1, characterized in that, The method for generating a continuous thickness distribution map by outputting the asphalt layer thickness value and its spatial location information at each scanning point is as follows: The thickness of the asphalt layer at each scanning point With planar spatial coordinates Connection, formation Data set; the scanning points are discrete measurement points collected at preset intervals when the millimeter-wave radar probe scans the target detection area; when generating the thickness continuous distribution map, spatial interpolation is used to interpolate the discrete thickness values ​​into a continuous surface, and the thickness distribution is identified by color gradient.

10. A method for implementing the asphalt pavement asphalt layer thickness detection device according to any one of claims 1-9, characterized in that, include: Step 1. Data Acquisition: The target detection area is synchronously scanned and measured using a millimeter-wave radar probe and a line laser displacement sensor integrated on the detection vehicle. The position, speed, and attitude information of the detection vehicle are recorded in real time through a GPS / IMU combined positioning system. The original radar echo sequence, original laser elevation data, and spatiotemporally synchronized position and attitude information are collected. Step 2. Data Analysis: Preprocess the original radar echo sequence to identify strong reflection points at the interfaces between corresponding asphalt layers; combine the attitude information of the inspection vehicle to generate corrected laser elevation point cloud data; based on the spatiotemporally synchronized position and attitude information, perform precise spatial registration between the corrected laser elevation point cloud data and the radar scanning point positions. Step 3. Data Processing: For each radar scanning point, extract the corrected laser elevation value of its vertical projection point on the road surface from the registered laser point cloud; calculate the electromagnetic wave propagation distance from the target interface at the radar scanning point to the millimeter-wave radar probe; calculate the asphalt layer thickness; output the asphalt layer thickness value and its spatial location information for each scanning point; and generate a continuous thickness distribution map.

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