Intelligent asphalt thickness detection method, device and system based on ground penetrating radar

Abstract

The invention provides an asphalt thickness intelligent detection method, device and system based on a ground penetrating radar, and relates to the technical field of asphalt thickness detection, and the method comprises the steps: enabling a multi-channel ground penetrating radar detection module to carry out the periodic lifting motion, obtaining calibration data, and building a noise signal model; single-point measurement is carried out, and the relative dielectric constant of the to-be-measured asphalt pavement is measured by adopting a common center method; adjusting the height of a ground penetrating radar antenna of the multi-channel ground penetrating radar detection module, and continuously measuring radar data of the asphalt road section; and based on the calibration data and the noise signal model, carrying out calibration, de-noising and smoothing processing on the radar data of the asphalt road section, tracking and generating an optimal boundary position through a dynamic programming algorithm, and calculating to obtain the pavement asphalt thickness distribution condition. The multi-channel ground penetrating radar and the lifting device work cooperatively, the relative dielectric constant is measured through the common midpoint method, the optimal height of the ground penetrating radar antenna is calculated, and the dynamic planning algorithm tracks and generates the optimal boundary for automatic processing.

Description

Technical Field

[0001] The present application relates to the technical field of asphalt thickness detection, and more specifically, to an intelligent asphalt thickness detection method, device and system based on ground penetrating radar. Background Art

[0002] Asphalt thickness detection has important application value in road construction and maintenance. With the increasing busyness of road traffic, the loss and aging of the asphalt layer are gradually aggravated, which directly affects the flatness and safety of the road. Through accurate detection of asphalt thickness, data support can be provided for road maintenance, potential damage or uneven problems can be discovered in time, premature or late road repairs can be avoided, and construction costs can be reduced. Traditional asphalt thickness detection methods mainly include core drilling method, ultrasonic method, etc. The core drilling method can provide accurate thickness data through sampling and analysis, but its operation is cumbersome and highly destructive, and cannot meet the needs of large-area road detection. Although the ultrasonic method can achieve non-destructive testing, it is greatly affected by the surface flatness and material uniformity, and is prone to excessive deviations. The methods for monitoring asphalt thickness based on ground penetrating radar in the prior art are: (1) A Chinese invention patent with publication number CN110258267A discloses a control method, device and system for determining asphalt paving thickness based on ground penetrating radar, comprising the following steps: a. determining the radar initial asphalt thickness value T0 of the target position based on n test channels, where n ≥ 1; b. calibrating the radar initial asphalt thickness value T0 based on the measured thickness value T of the target position to determine the calibrated wave velocity c of the target position, and determining the real-time thickness H of the asphalt paving based on at least the calibrated wave velocity of the target position. i .

[0003] (2) The Chinese invention patent with publication number CN115236659A discloses a ground penetrating radar and a radar-based asphalt pavement paving thickness monitoring and evaluation method, including a host computer PC, a main control system and an antenna system; the host computer PC is used to configure the working parameters of the ground penetrating radar, control the start and stop of the equipment, receive radar echo data, generate radar data files and perform data processing and imaging on the echo data; the main control system is based on the "MCU-CPLD" dual control unit architecture; the antenna system consists of multiple detection channels, each detection channel includes a transmitting circuit, a receiving circuit and an antenna probe; the antenna probe is connected to the main control system through a control cable.

[0004] Both of the above test methods rely on the measured thickness value to adjust the calculation parameters of the asphalt thickness. They are still variants of the aforementioned core drilling method. They still have the problems of being cumbersome and highly destructive, and cannot meet the needs of large-area road inspection. Summary of the invention

[0005] To solve the above problems, the technical solution adopted in this application is an intelligent asphalt thickness detection method based on ground penetrating radar, which includes the following steps: Obtain calibration data: Make the multi-channel ground penetrating radar detection module perform periodic lifting and lowering motion to obtain calibration data and establish a noise signal model; Measure the relative dielectric constant: Move the multi-channel ground penetrating radar detection module, select measurement points at equal intervals, perform single-point measurement, and use the common center method to measure the relative dielectric constant of the asphalt pavement to be tested; Obtain radar data of asphalt road sections: Based on the relative dielectric constant of the asphalt road surface, adjust the height of the ground-penetrating radar antenna of the multi-channel ground-penetrating radar detection module, and uniformly translate the multi-channel ground-penetrating radar detection module to continuously measure the radar data of the asphalt road section; Data processing: Based on the calibration data and noise signal model, the radar data of the asphalt road section is calibrated, denoised and smoothed. The optimal dividing line position is tracked and generated through the dynamic programming algorithm, and the distribution of asphalt thickness of the road surface is calculated.

[0006] Optionally, obtaining calibration data includes collecting radar signal strength and ground penetrating radar antenna height during the periodic lifting and lowering movement of the multi-channel ground penetrating radar detection module, establishing an attenuation model, and calculating the attenuation coefficient. The attenuation model is: ; In the formula, Indicates at time At time , the radar signal strength after attenuation is Indicates the radar signal strength at the reference altitude, represents the attenuation coefficient, Indicates time The height of the ground penetrating radar antenna at any moment; The noise signal model is established as: ; In the formula, For noise, is the amplitude of the noise, is the frequency of the noise, Indicates time, Indicates time The height of the ground penetrating radar antenna at each moment.

[0007] Optionally, the co-center method is used to measure the relative dielectric constant of the asphalt pavement to be tested, including using a multi-channel ground penetrating radar, enabling one transmitting channel to transmit electromagnetic wave signals for each test, enabling the first and last two receiving channels to receive electromagnetic wave signals, and calculating the relative dielectric constant of the asphalt pavement based on the time it takes for the two receiving channels to receive the reflected waves from the interface below the asphalt layer, and the length of the transmitting antenna from the two receiving antennas. Multiple measurements are performed at each measuring point using multiple transmitting channels, and finally the average value of the relative dielectric constants of the asphalt pavement measured by all measuring points and multiple transmitting channels at each measuring point is taken as the relative dielectric constant of the asphalt pavement to be tested.

[0008] Optionally, the calculation of the height above the ground of the multi-channel ground penetrating radar detection module includes making the antenna height satisfy Formula 1: ; In formula 1, is the center frequency of electromagnetic waves emitted by the ground penetrating radar, is the speed of light, is the relative dielectric constant of the asphalt pavement to be tested; And it needs to satisfy formula 2: ; In formula 2, is the thickness of the asphalt layer, Take the smallest integer that satisfies formula 1 .

[0009] Optionally, calibrating, denoising and smoothing the asphalt road section radar data includes calibrating and denoising according to the following formula: ; is the attenuation coefficient, is the ground penetrating radar antenna height, is the noise derived from the calibration data; after calibration and denoising, the grayscale value of the image is obtained by mapping the radar data of the asphalt road section according to the following formula: : ; In the formula, is the scaling factor, we have: ; In the formula, is the standard deviation of the signal; using a moving average filter, the signal strength after smoothing is expressed as: ; In the formula, is the window size of the moving average filter, Indicates at location The signal strength before smoothing.

[0010] Optionally, tracking and generating the optimal boundary line position by a dynamic programming algorithm includes: manually marking three anchor points on the boundary line, the x-axis is the horizontal scanning direction, and the range is ; The y-axis is the depth direction, and the x-axis coordinates of the three anchor points must satisfy: ; In the formula, , , The horizontal coordinates of the left anchor point, the middle anchor point, and the right anchor point are respectively; manually select the interface range under the asphalt layer in the area that meets the x-axis, and select the points in the local window that meet the signal stability and phase continuity: ; In the formula, represents the signal stability constraint, represents the mean value, A represents the signal amplitude, Represents the mean of the square of the signal amplitude in the entire area, Indicated by point As the center, the x-axis direction is from arrive , the y-axis direction is from arrive The signal amplitude matrix in the rectangular area of ​​; represents the phase continuity constraint, Indicates the sampling point index in the y-axis direction. Indicates at point The phase value at Indicates at point The phase value at the point where the constraint is satisfied; among the points where the signal amplitude in each x-axis partition is selected The largest point is used as the anchor point. Indicates at point The signal amplitude at ; Calculate the cost functions of the dividing line cost, smoothness cost and distance cost, and combine them to get a comprehensive cost function. For each column, it is necessary to calculate the optimal dividing line position of the current column based on the state of the previous column and the comprehensive cost function of the current column; when the last column is calculated, the termination condition is to find the minimum cost of the column. After obtaining the optimal dividing line position of the column, backtrack from the last column to the first column to find the optimal dividing line position of each column.

[0011] Optionally, the distribution of asphalt thickness of the road surface is calculated based on the following formula: ; In the formula, is the position of the dividing line in the B-scan image, is the corresponding time point, for depth; is the propagation speed of electromagnetic wave signal in asphalt, Satisfy the formula: ; In the formula, is the speed of light, is the relative dielectric constant of asphalt.

[0012] The present application also provides an intelligent detection device for asphalt thickness based on ground penetrating radar, which is suitable for executing any of the aforementioned intelligent detection methods for asphalt thickness based on ground penetrating radar.

[0013] Optionally, it includes: a multi-channel ground penetrating radar detection module, a lifting module, a power module and a control module, and the multi-channel ground penetrating radar detection module includes a ground penetrating radar, an RTK positioning module and a wheel odometer.

[0014] The present application also provides an intelligent detection system for asphalt thickness based on ground penetrating radar, which has a program module corresponding to any of the aforementioned intelligent detection methods for asphalt thickness based on ground penetrating radar, including: Data calibration module: used to make the multi-channel ground penetrating radar detection module perform periodic lifting and lowering movements to obtain calibration data and establish a noise signal model; Relative dielectric constant measurement module: used for single-point measurement and using the concentric method to measure the relative dielectric constant of the asphalt pavement to be tested; Asphalt road section radar data acquisition module: used to adjust the height of the ground penetrating radar antenna of the multi-channel ground penetrating radar detection module based on the relative dielectric constant of the asphalt road surface, and obtain the asphalt road section radar data continuously measured by the uniform translation multi-channel ground penetrating radar detection module; Data processing module: used to calibrate, denoise and smooth the radar data of asphalt road sections based on calibration data and noise signal models, track and generate the optimal dividing line position through dynamic programming algorithm, and calculate the distribution of asphalt thickness of the road surface.

[0015] The beneficial effects of the intelligent asphalt thickness detection method, device and system based on ground penetrating radar provided in this application are: This application uses a multi-channel ground penetrating radar detection module to work in conjunction with a lifting module. By controlling the periodic lifting of the lifting device, preliminary calibration data is collected and recorded for subsequent data processing of formal detection data. Combined with the working characteristics of the multi-channel ground penetrating radar detection module, the common center point method is used to measure the relative dielectric constant of the asphalt layer, and the propagation speed of electromagnetic waves in the asphalt layer is calculated, providing a basis for calculating the optimal height of the ground penetrating radar antenna from the ground and the thickness of the asphalt layer. The present invention automatically processes ground penetrating radar data by tracking and generating the optimal dividing line position through a dynamic programming algorithm, calibrates the lower interface dividing line of the asphalt layer according to the cost function and boundary conditions, obtains the propagation time of electromagnetic waves in the asphalt layer, and then calculates the thickness distribution of the asphalt layer. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art.

[0017] Figure 1 It is a schematic diagram of the structure of an intelligent asphalt thickness detection device based on ground penetrating radar in an embodiment of the present invention; Figure 2 It is a left view of the structure of an intelligent detection device for asphalt thickness based on ground penetrating radar in an embodiment of the present invention; Figure 3 is a schematic diagram of the arrangement of ground penetrating radar antennas in an embodiment of the present invention; Figure 4 Schematic diagram of measuring the relative dielectric constant of an asphalt layer using the common center point method in an embodiment of the present invention; Figure 5 is a working flow chart of an intelligent asphalt thickness detection method based on ground penetrating radar in an embodiment of the present invention; Figure 6 It is a data processing flow chart of the intelligent detection method of asphalt thickness based on ground penetrating radar in an embodiment of the present invention.

[0018] Explanation of the reference numerals: 11-hand support; 12-connecting rod; 13-triangular support; 14-chassis support; 15-tire; 2-hydraulic lifting device; 3-control host; 4-battery; 51-ground penetrating radar; 511-power interface; 512-communication interface; 513-RTK interface; 514-odometer interface; 52-RTK positioning module; 53-wheel odometer; 6-receiving channel; 7-transmitting channel; 8-ground; 9-asphalt layer; 10-base layer. DETAILED DESCRIPTION

[0019] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0020] Example 1 Combination Figure 1-Figure 4 As shown, the present invention provides an intelligent detection device for asphalt thickness based on ground penetrating radar, including a multi-channel ground penetrating radar detection module, a lifting module, a power module and a control module. The multi-channel ground penetrating radar detection module includes a ground penetrating radar, an RTK positioning module and a wheel odometer.

[0021] In this embodiment, the multi-channel ground penetrating radar detection module, the lifting module, the power module and the control module are installed on the cart bracket, the lifting module is a hydraulic lifting device 2, the control module is a control host 3, and the power module is a battery 4; The trolley bracket includes a hand support bracket 11, a connecting rod 12, a triangular bracket 13, a chassis bracket 14 and a tire 15; the hand support bracket 11 supports the control host 3; the connecting rod 12 connects the hand support bracket 11 and the triangular bracket 13; the battery 4 is installed on the triangular bracket 13; the chassis bracket 14 fixes the ground penetrating radar 51 and is installed with a hydraulic lifting device 2; the above brackets are all made of stainless steel, hollow inside, and the connection points are fixed by welding.

[0022] The hydraulic lifting device 2 is connected to the ground penetrating radar 51 through four hydraulic rods and is installed above the chassis; the control host 3 is connected to the ground penetrating radar 51 through a network cable; the battery 4 is connected to the control host 3 and the multi-channel ground penetrating radar detection module through a power cable, and the above cables are all embedded in the bracket.

[0023] The hydraulic lifting device 2 is used to adjust the height of the antenna of the ground penetrating radar 51 relative to the road surface; the hydraulic lifting device 2 is driven by the hydraulic system to enable the ground penetrating radar 51 to rise and fall during the detection process, and record the ground penetrating radar data when the height of the ground penetrating radar antenna changes; secondly, the working height of the ground penetrating radar can be flexibly adjusted according to different road conditions and detection requirements to ensure that the ground penetrating radar antenna maintains an optimal detection distance with the asphalt road surface; The control host 3 is used to configure the trigger distance, time window, number of sampling points and other working parameters of the ground penetrating radar detection system 5, control the working mode of the ground penetrating radar and the start and stop of the detection, and perform ground penetrating radar data processing after the detection; the control host 3 controls three working modes of the ground penetrating radar detection system 5, namely, the calibration mode, the single point mode, and the detection mode. In the calibration mode, the hydraulic lifting device 2 is started, and the ground penetrating radar data is recorded as the sample data of the data processing stage. The detection mode is to record the data of the asphalt road section to be detected, and the single point mode is to measure the relative dielectric constant of the single point asphalt layer 9; The multi-channel ground penetrating radar detection module includes a ground penetrating radar 51, an RTK positioning module 52, and a wheel odometer 53. The ground penetrating radar 51 has four interfaces, namely a power interface 511, a communication interface 512, an RTK interface 513, and an odometer interface 514; the RTK positioning module 52 is installed above the ground penetrating radar 51 and is connected to the RTK interface 513; the wheel odometer is installed on a tire center axis in the tire 15 and is connected to the odometer interface 514.

[0024] The ground penetrating radar detection system 5 uses a short pulse radar method to test the road thickness. The asphalt thickness is usually less than 10 cm. The antenna of the ground penetrating radar 51 is a replaceable module. The optional range of the antenna center frequency is 1 GHz~2 GHz. It adopts a multi-channel working mode and works in a total of 14 channels through 7 transmitting antennas and 8 receiving antennas. The RTK positioning module 52 is a differential real-time positioning module that provides centimeter-level positioning coordinates for the system to record the position of the marked points and locate the detection route; the wheel odometer 53 uses a 1024 sampling point encoder and rotates coaxially with the tire center as a trigger condition for the ground penetrating radar 51.

[0025] Example 2 like Figure 3-Figure 5 As shown, the present invention provides an intelligent detection method for asphalt thickness based on ground penetrating radar.

[0026] Obtain calibration data: place the multi-channel ground penetrating radar detection module above the asphalt road surface to be detected, and under the control of the control module, use the lifting module to make the multi-channel ground penetrating radar detection module perform periodic lifting and lowering movements to obtain calibration data and establish a noise signal model; The control host 3 controls the switching of the working mode of the ground penetrating radar to the calibration mode, installs the ground penetrating radar detection system 5 and the hydraulic lifting device 2 on the cart bracket 1, and positions it above the asphalt pavement to be detected; starts the hydraulic lifting device 2, keeps the ground penetrating radar antenna 1 cm to 15 cm away from the ground 8, performs periodic slow lifting and lowering movements, and records the ground penetrating radar calibration data.

[0027] The function of the hydraulic lifting device 2 is to adjust the distance between the ground penetrating radar and the asphalt road surface, thereby changing the propagation path of the radar wave. The periodic lifting movement in this process can ensure that the radar can receive different reflected signals from the asphalt road surface at multiple height positions. The movement control of the lifting device is realized through the hydraulic system to control the amplitude and frequency of its lifting to ensure that the ground penetrating radar can collect reflected signals multiple times within a stable height range; the height of the antenna During the calibration phase changes, the radar signal strength It will decay exponentially with the increase of antenna height. The attenuation model is used to express the effect of height change on signal amplitude: ; in, Indicates at time At time , the radar signal strength after attenuation is Indicates the radar signal strength at a reference altitude. The reference altitude refers to selecting a known altitude. is the attenuation coefficient, which reflects the attenuation rate of the signal as the height changes. Indicates time The height of the ground penetrating radar antenna at each moment; by recording the and radar signal strength , the attenuation coefficient can be estimated , thus deriving how to adjust the signal according to the fixed antenna height during the formal detection phase; Decompose the radar signal, use Fourier transform to obtain the frequency domain signal or wavelet transform to obtain the time-frequency domain signal, determine the amplitude of the noise signal by analyzing the amplitude of the frequency domain signal or the time-frequency domain signal, identify the main frequency components of the noise signal, and analyze the phase change of the noise signal.

[0028] For example, as a feasible implementation, firstly, the radar signal is subjected to a fast Fourier transform to obtain a spectrum, and the signal peak is marked by threshold detection or peak search (such as 3 times the standard deviation higher than the average amplitude), and the remaining frequency band is regarded as the noise-dominated area, and the frequency component with the largest amplitude is identified as the frequency of the noise. The average power spectral density (PSD) is calculated in the noise-dominated frequency band and converted into the time domain noise amplitude (RMS value).

[0029] Based on the above antenna height information, for the noise at each height, a sine fit is used to obtain the model Extract the phase, where is the amplitude of the noise, is the frequency of the noise, sums up the phase values ​​corresponding to different heights, and fits the functional form of the phase shift related to height.

[0030] According to the calibration data, a noise signal model is established to obtain the noise and antenna height The relationship between: ; In the formula, is the amplitude of the noise, is the frequency of the noise, It is a phase shift related to the height. The noise characteristics at different heights are estimated through the noise data in the calibration phase, and the reflection intensity data at different propagation times are obtained as calibration data, which provides a basis for removing noise and filtering the actual detection data. Measure the relative dielectric constant: Move the multi-channel ground penetrating radar detection module, select measurement points at equal intervals, perform single-point measurement, and use the common center method to measure the relative dielectric constant of the asphalt pavement to be tested; The common center point method is used to measure the relative dielectric constant of the asphalt pavement, specifically including the use of a multi-channel ground penetrating radar. In this embodiment, there are 7 transmitting channels 7 and 8 receiving channels 6, and the transmitting channels 7 and the receiving channels 6 are arranged horizontally. In the common center point method for measuring the relative dielectric constant of the asphalt pavement, the control host (3) controls the switching of the working mode of the ground penetrating radar to the single point mode, only one transmitting channel 7 is enabled to transmit the electromagnetic wave signal, and the first and last receiving channels 6 are enabled to receive the electromagnetic wave signal. The electromagnetic wave propagates in the asphalt layer 9 above the base layer 10 to satisfy the formula: ; in, is the propagation speed of electromagnetic waves in the asphalt layer 9, is the speed of light, is the relative dielectric constant of asphalt, which can be obtained based on the geometric relationship of underground media: ; in, is the thickness of the asphalt layer 9, and are the time when the two receiving channels 6 receive the reflected waves from the interface under the asphalt layer 9, and are the lengths of the transmitting antenna from the two receiving antennas, respectively, and we can solve for: ; in, is the relative dielectric constant of the asphalt layer 9 measured when the first transmitting channel 7 is used; the ground penetrating radar used in the present invention has a total of 7 transmitting channels 7 and 8 receiving channels 6, and uses 7 transmitting channels 7 to transmit signals and the first and eighth receiving channels 6 to receive signals, so in the first The relative dielectric constant measured when the transmission channel is 7 for: ; In the formula, and Respectively indicate the use of When the transmitting channel 7 receives the reflected wave from the interface under the asphalt layer 9, the two receiving channels 6 receive the reflected wave from the interface under the asphalt layer 9. and Respectively indicate the use of When there are 7 transmitting channels, the distance between the transmitting antenna and the two receiving antennas is calculated. The relative dielectric constant of the asphalt layer 9 measured at 7 channels at 5 measuring points is averaged: ; Through the above steps, the relative dielectric constant of the asphalt layer 9 is calculated. , to provide data support for determining the placement height of the ground penetrating radar antenna and the propagation speed of electromagnetic waves in the asphalt layer 9; Obtain radar data of asphalt road sections: Based on the relative dielectric constant of the asphalt road surface, the height of the ground-penetrating radar antenna of the multi-channel ground-penetrating radar detection module is adjusted by using the lifting module, and the multi-channel ground-penetrating radar detection module is uniformly translated to continuously measure the radar data of the asphalt road section; Calculate and adjust the optimal height of the ground penetrating radar antenna, including: To avoid the interference of the near-field effect of the ground penetrating radar antenna on the signal, ensure that the electromagnetic wave enters the far field area, reduce the near-field coupling noise, and adjust the height of the antenna. Need to satisfy formula 1: ; In the formula, is the center frequency of electromagnetic waves emitted by the ground penetrating radar, is the speed of light, is the relative dielectric constant of asphalt calculated in claim 7; In order to achieve beam focusing, the reflected wave of the ground penetrating radar electromagnetic wave at the interface below the asphalt layer 9 is constructively superimposed with the direct wave, and the phase difference between the two waves is an odd number of half a wavelength. Need to satisfy formula 2: ; in, is the thickness of the asphalt layer, Take the smallest integer that satisfies formula 1 , it can be determined The value of is: ; Step 3 continuously detects the asphalt pavement, specifically including: starting the ground penetrating radar system, and controlling the host (3) to switch the working mode of the ground penetrating radar to the detection mode; pushing the cart support to move forward at a constant speed along the detection section, and the ground penetrating radar system will continue to emit electromagnetic waves and receive reflected signals; all collected radar data including the propagation time of the reflected wave, the reflection intensity and the dielectric constant between different layers, etc., will be recorded and stored in the data device in real time to provide data support for subsequent thickness analysis; Data processing: Based on the calibration data and noise signal model, the radar data of the asphalt road section is calibrated, denoised and smoothed. The dynamic programming algorithm tracks and generates the optimal dividing line position, and the distribution of asphalt thickness of the road surface is calculated.

[0031] like Figure 6As shown in the figure, the data processing of ground penetrating radar data specifically includes: first, the formal detection data is calibrated and denoised through the height and signal relationship model obtained in the calibration stage, and the signal after calibration and denoising is for: ; in, is the attenuation coefficient, is the calculated GPR antenna height, is the noise derived from the calibration data; After denoising the ground penetrating radar echo signal, the obtained signal It is a time domain signal, which needs to be mapped to the grayscale value of the image ; Assume that in the B-scan image, each point Corresponding to a time point , through logarithmic mapping, the time domain signal Map the grayscale values ​​of an image: ; in, Indicates that in the B-scan image, each point The corresponding time point, Indicates at a point in time The time domain signal intensity at , corresponding to the image coordinates , is the scaling factor, which controls the gain of the signal after logarithmic transformation. By taking the value of the dynamic range of the signal, strong signals can be compressed and weak signals can be enhanced: ; in, is the standard deviation of the signal. The standard deviation of the signal reflects the degree of change in the signal amplitude. The larger the standard deviation, the larger the dynamic range of the signal. Therefore, a smaller standard deviation is needed. To compress the signal range; In the calculation of the image grayscale value, the signal needs to be smoothed to reduce the impact of noise on the image quality. Using a moving average filter, the signal strength after smoothing can be expressed as: ; In the formula, is the window size of the moving average filter, Indicates at location In the detected B-scan image, three points on the dividing line are manually marked, and the dynamic programming algorithm is used to track and generate the complete dividing line.

[0032] The dynamic programming algorithm tracks and generates the optimal dividing line position, including: manually marking three anchor points on the dividing line, the x-axis is the horizontal scanning direction, and the range is ; The y-axis is the depth direction, and the x-axis coordinates of the three anchor points must satisfy: ; In the formula, , , The horizontal coordinates of the left anchor point, the middle anchor point, and the right anchor point are respectively; the interface range of the asphalt layer 9 is manually selected in the area satisfying the x-axis, and the points satisfying the signal stability and phase continuity in the local window are screened: ; In the formula, represents the signal stability constraint, which constrains the ratio of the local signal energy to the global energy at Δx=2 and Δy=5 to be greater than 2. represents the mean value, represents the signal amplitude, Represents the mean of the square of the signal amplitude in the entire area, which serves as the benchmark for global energy. Indicated by point As the center, the x-axis direction is from arrive , the y-axis direction is from arrive The signal amplitude matrix in the rectangular area of ​​; Indicates the phase continuity constraint, which constrains the cosine mean of five consecutive y-axis sampling points to be greater than 0.8. Represents the sampling point index in the y-axis direction, which is used to traverse 5 consecutive sampling points. Indicates at point The phase value at Indicates at point The phase value at the point where the constraint is satisfied; among the points where the signal amplitude in each x-axis partition is selected The largest point is used as the anchor point. Indicates at point The signal amplitude at ; Calculate the cost functions of the dividing line cost, smoothness cost and distance cost, and combine them to get a comprehensive cost function. For each column, it is necessary to calculate the optimal dividing line position of the current column based on the state of the previous column and the comprehensive cost function of the current column; when the last column is calculated, the termination condition is to find the minimum cost of the column. After obtaining the optimal dividing line position of the column, backtrack from the last column to the first column to find the optimal dividing line position of each column.

[0033] Due to the difference in underground media, the phase of the reflected wave changes during the propagation of electromagnetic waves. The medium boundary is reflected as a grayscale mutation in the B-scan grayscale image. Part of the cost function in the dynamic programming algorithm reflects the change in image grayscale. Assume is the position in the image The gray value of the dividing line can be expressed as: ; in, and They are the positions of the dividing line between the previous column and the current column, and are the gray values ​​of the corresponding positions, and the position of the mutation is usually the location of the dividing line; To ensure that the dividing line does not vary much between consecutive columns, the smoothness cost can be expressed as ; in, is the smoothing coefficient, which controls the smoothness of the dividing line; The three points manually marked in the B-scan image affect the final boundary line. The cost function considers the position constraints of these marked points. is the true boundary position, then the cost function can include the distance cost between this point: ; in, is the constraint coefficient, controlling the influence of manually marked points; Combining the above three cost functions, we get the final cost function: ; In the process of dynamic programming, it is necessary to initialize the boundary conditions. Since several points on the dividing line have been marked in the image, these manually marked points are used as initial conditions. The dividing line is marked , then the boundary conditions can be initialized as: ; For each column , it is necessary to calculate the optimal dividing line position of the current column based on the state of the previous column and the cost function of the current column. The recursive formula is: ; in, Is in the list Upper dividing line position The minimum cost when Indicates in the previous column The dividing line is located at The cumulative minimum cost when From the previous column Move to the current column The cost of location; When the last column is calculated, the termination condition is to find the minimum cost of the column, that is: ; In the formula, represents the index of the last column of the image, Indicates that in the last column The possible position of the dividing line at is used to traverse all possible dividing line positions in the column to find the optimal position with the minimum cumulative cost. Indicates that in the last column The optimal dividing line position.

[0034] After obtaining the optimal dividing line position of the column, find the optimal dividing line position of each column by backtracking from the last column to the first column. The backtracking process starts from the optimal position of the last column and gradually returns to the first column. When backtracking, choose the path with the minimum cost and obtain the optimal dividing line position of each column: ; In the formula, Indicates the optimal dividing line position of each column. The thickness distribution of the asphalt layer 9 of the road to be tested, specifically including: the position of the known dividing line in the B-scan image and the corresponding time points , the depth can be calculated : ; in, is the propagation speed of electromagnetic wave signals in the medium. is the speed of light, is the relative dielectric constant of the asphalt layer 9, from which it can be deduced: ; The thickness distribution of the asphalt layer 9 of the road surface to be tested is calculated.

[0035] Example 3 The control host 3 configures the working parameters of the ground penetrating radar detection system 5: the trigger distance is 1 cm, the time window is 10 nanoseconds, the number of sampling points is 128 points, the ground penetrating radar detection system 5 selects the antenna center frequency of the ground penetrating radar as 1 GHz, and the wheel odometer 53 uses a 1024 sampling point encoder; Select asphalt test section A, install the ground penetrating radar detection system, control the host to switch the working mode of the ground penetrating radar to the calibration mode, record the ground penetrating radar calibration data, and establish the signal attenuation model and noise model ; In the asphalt test section A, 5 measuring points are selected and marked evenly spaced, and the relative dielectric constant of the asphalt layer 9 is measured using the common center point method. The electromagnetic wave propagation speed is calculated using the wave speed formula. The example data of the first point is: Transmitter channel: Tx3 (Channel 3) Receiving channels: Rx1 (first channel) and Rx8 (last channel) Channel spacing: horizontal spacing between Tx3 and Rx1: 25cm; horizontal spacing between Tx3 and Rx8: 35cm Measurement data: The time it takes for the electromagnetic wave to reach Rx1 is 2ns; the time it takes for the electromagnetic wave to reach Rx8 is 3.2ns The relative dielectric constant is calculated: ; The average relative dielectric constant of the asphalt layer 9 of the asphalt test section A is calculated to be 4.0, and the electromagnetic wave velocity is calculated as: ; The relative dielectric constant of asphalt layer 9 is used to calculate and adjust the optimal height of the ground penetrating radar antenna and the lower limit of the antenna height: ; Calculate the antenna height that satisfies the beam focusing condition, taking n=1: ; Verify that 12.5mm<23.9mm does not meet the condition; take n=2: ; Verify that 37.5mm>23.9mm, if the condition is met, then select 37.5mm; The trolley is pushed forward at a constant speed, and the ground penetrating radar detection system is started to continuously detect along the asphalt road surface to be detected. The ground penetrating radar data of the asphalt road section to be detected is recorded and stored as the original data of the road section; The collected ground penetrating radar data is processed according to the calibration data. , to filter and enhance the data, first through the radar height and the experimentally obtained attenuation coefficient , through the formula , the original signal is dynamically compensated; then, logarithmic mapping is used Convert the time domain signal to a grayscale image, where the scaling factor is Adaptively adjust according to the signal standard deviation. Finally, use the moving average filter The image is smoothed, and the window size N is preferably 5-15 to suppress noise and improve the thickness inversion accuracy.

[0036] Next, the boundary line tracking algorithm of dynamic programming is used to calibrate the lower interface between the ground 8 and the asphalt layer 9, obtain the propagation time of the electromagnetic wave in the asphalt layer 9, and finally calculate the thickness distribution of the asphalt layer 9 of the road surface to be tested. The steps are as follows: (1) Manually mark the left endpoint, middle reference point, and right endpoint at 1 / 16, 1 / 2, and 15 / 16 of the B-scan image, respectively; (2) Constructing a comprehensive cost function , including grayscale mutation cost , smoothness cost (smoothing coefficient λ = 0.5~1.5) and constraint cost (constraint coefficient μ=0.3~0.8); (3) Calculate the minimum cumulative cost column by column from left to right, allowing for an offset in the ordinates of adjacent columns Pixels; (4) Backtracking to generate the optimal dividing line; (5) Calculating the thickness of the asphalt layer 9 based on the position of the dividing line and the wave velocity of the electromagnetic wave in the asphalt layer 9; Example 4 The control host 3 configures the working parameters of the ground penetrating radar detection system 5: the trigger distance is 1 cm, the time window is 10 nanoseconds, the number of sampling points is 128 points, the ground penetrating radar detection system 5 selects the antenna center frequency of the ground penetrating radar as 2 GHz, and the wheel odometer 53 uses a 1024 sampling point encoder; Select asphalt test section B, install the ground penetrating radar detection system, control the host to switch the working mode of the ground penetrating radar to the calibration mode, record the ground penetrating radar calibration data, and establish the signal attenuation model and noise model ; In the asphalt test section B, 5 measuring points are selected and marked evenly spaced, and the relative dielectric constant of the asphalt layer 9 is measured using the common center point method. The electromagnetic wave propagation speed is calculated using the wave speed formula. The example data of the first point is: Transmitter channel: Tx2 (Channel 2) Receiving channels: Rx1 (first channel) and Rx8 (last channel) Channel spacing: horizontal spacing between Tx2 and Rx1: 20cm; horizontal spacing between Tx2 and Rx8: 50cm Measurement data: The time it takes for the electromagnetic wave to reach Rx1 is 1.5ns; the time it takes for the electromagnetic wave to reach Rx8 is 3.2ns The relative dielectric constant is calculated: ; The average relative dielectric constant of the asphalt layer 9 of the asphalt test section A is calculated to be 4.0, and the electromagnetic wave velocity is calculated as: ; The relative dielectric constant of asphalt layer 9 is used to calculate and adjust the optimal height of the ground penetrating radar antenna and the lower limit of the antenna height: ; Calculate the antenna height that satisfies the beam focusing condition, substituting h=0.06m and taking n=1: ; Verify that 16.04mm>12.7mm, if the condition is met, then select 16mm; The trolley is pushed forward at a constant speed, the ground penetrating radar detection system is started, and continuous detection is performed along the asphalt road surface to be detected. The ground penetrating radar data of the asphalt road section to be detected is recorded and stored and will be used as the original data of the section.

[0037] The collected ground penetrating radar data is processed according to the calibration data. , to filter and enhance the data, first through the radar height and the experimentally obtained attenuation coefficient , through the formula , the original signal is dynamically compensated; then, logarithmic mapping is used Convert the time domain signal to a grayscale image, where the scaling factor is Adaptively adjust according to the signal standard deviation. Finally, use the moving average filter The image is smoothed, and the window size N is preferably 5-15 to suppress noise and improve the thickness inversion accuracy.

[0038] Next, the boundary line tracking algorithm of dynamic programming is used to calibrate the lower interface between the ground 8 and the asphalt layer 9, obtain the propagation time of the electromagnetic wave in the asphalt layer 9, and finally calculate the thickness distribution of the asphalt layer 9 of the road surface to be tested. The steps are as follows: (1) Manually mark the left endpoint, middle reference point, and right endpoint at 1 / 16, 1 / 2, and 15 / 16 of the B-scan image, respectively; (2) Constructing a comprehensive cost function , including grayscale mutation cost , smoothness cost (smoothing coefficient λ = 0.5~1.5) and constraint cost (constraint coefficient μ = 0.3 ~ 0.8); (3) Calculate the minimum cumulative cost column by column from left to right, allowing for an offset in the ordinates of adjacent columns Pixels; (4) Backtracking to generate the optimal dividing line; (5) Calculating the thickness of the asphalt layer 9 based on the position of the dividing line and the wave velocity of the electromagnetic wave in the asphalt layer 9; Performance Verification: In order to verify the effectiveness of the dynamic programming dividing line method, a highway asphalt layer 9 detection scene was selected for comparative experiments: Test conditions: Dataset: 100 sets of B-scan images (including manually annotated real dividing lines); Comparison methods: traditional Canny edge detection method, fixed threshold segmentation method; Parameter settings: smoothing coefficient λ=0.8, constraint coefficient μ=0.5, window offset Δ=5 pixels.

[0039] Test results: Positioning accuracy: The average error of the demarcation line of the method of the present invention is 1.2mm (the traditional method is 4.5mm), and the accuracy is improved by 73%; Noise resistance: When the signal-to-noise ratio is ≥15dB, the error rate is stable within 2% (the error rate of the traditional method is >10%); Computational efficiency: Single-frame image processing takes ≤ 0.8 seconds (traditional methods ≥ 1.5 seconds).

[0040] Conclusion: This paper significantly improves the accuracy and speed of boundary line positioning by combining dynamic programming algorithm with manual labeling constraints.

[0041] Example 5 The asphalt thickness detection method based on ground penetrating radar proposed in the present invention is the underlying technical core of the present invention, and various products can be derived based on the algorithm.

[0042] Based on the method proposed in the present invention, a ground-penetrating radar-based intelligent asphalt thickness detection system is developed using a programming language. The system has a program module corresponding to the steps of the above technical solution, and executes the steps of the above ground-penetrating radar-based asphalt thickness method during operation, which at least includes: Data calibration module: used to make the multi-channel ground penetrating radar detection module perform periodic lifting and lowering movements to obtain calibration data and establish a noise signal model; Relative dielectric constant measurement module: used for single-point measurement and using the concentric method to measure the relative dielectric constant of the asphalt pavement to be tested; Asphalt road section radar data acquisition module: used to adjust the height of the ground penetrating radar antenna of the multi-channel ground penetrating radar detection module based on the relative dielectric constant of the asphalt road surface, and obtain the asphalt road section radar data continuously measured by the uniform translation multi-channel ground penetrating radar detection module; Data processing module: used to calibrate, denoise and smooth the radar data of asphalt road sections based on calibration data and noise signal models, track and generate the optimal dividing line position through dynamic programming algorithm, and calculate the distribution of asphalt thickness of the road surface.

[0043] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An intelligent asphalt thickness detection method based on ground penetrating radar, characterized in that: The following steps are involved: Obtain calibration data: Make the multi-channel ground penetrating radar detection module perform periodic lifting and lowering motion to obtain calibration data and establish a noise signal model; Measure the relative dielectric constant: Move the multi-channel ground penetrating radar detection module, select measurement points at equal intervals, perform single-point measurement, and use the common center method to measure the relative dielectric constant of the asphalt pavement to be tested; Obtain radar data of asphalt road sections: Based on the relative dielectric constant of the asphalt road surface, adjust the height of the ground-penetrating radar antenna of the multi-channel ground-penetrating radar detection module, and uniformly translate the multi-channel ground-penetrating radar detection module to continuously measure the radar data of the asphalt road section; Data processing: Based on the calibration data and noise signal model, the radar data of the asphalt road section is calibrated, denoised and smoothed. The optimal dividing line position is tracked and generated through the dynamic programming algorithm, and the distribution of asphalt thickness of the road surface is calculated.

2. The intelligent asphalt thickness detection method based on ground penetrating radar according to claim 1 is characterized in that: The acquisition of calibration data includes collecting radar signal strength and ground penetrating radar antenna height during the periodic lifting and lowering movement of the multi-channel ground penetrating radar detection module, establishing an attenuation model, and calculating the attenuation coefficient. The attenuation model is: ； In the formula, Indicates at time At time , the radar signal strength after attenuation is Indicates the radar signal strength at the reference altitude, represents the attenuation coefficient, Indicates time The height of the ground penetrating radar antenna at any moment; The noise signal model is established as follows: ； In the formula, For noise, is the amplitude of the noise, is the frequency of the noise, Indicates time, Indicates time The height of the ground penetrating radar antenna at each moment.

3. The intelligent asphalt thickness detection method based on ground penetrating radar according to claim 1 is characterized in that: The common center method is used to measure the relative dielectric constant of the asphalt pavement to be tested, including the use of a multi-channel ground penetrating radar. Each test enables one transmitting channel to transmit electromagnetic wave signals, and enables the first and last two receiving channels to receive electromagnetic wave signals. The relative dielectric constant of the asphalt pavement is calculated based on the time it takes for the two receiving channels to receive the reflected waves from the interface below the asphalt layer, and the length of the transmitting antenna from the two receiving antennas. Multiple measurements are performed at each measuring point using multiple transmitting channels, and finally the average value of the relative dielectric constants of the asphalt pavement measured by all measuring points and multiple transmitting channels at each measuring point is taken as the relative dielectric constant of the asphalt pavement to be tested.

4. The intelligent asphalt thickness detection method based on ground penetrating radar according to claim 3 is characterized in that: The calculation of the height above the ground of the multi-channel ground penetrating radar detection module includes making the antenna height satisfy equation 1: ； In formula 1, is the center frequency of electromagnetic waves emitted by the ground penetrating radar, is the speed of light, is the relative dielectric constant of the asphalt pavement to be tested; And it needs to satisfy formula 2: ； In formula 2, is the thickness of the asphalt layer, Take the smallest integer that satisfies formula 1 .

5. The intelligent asphalt thickness detection method based on ground penetrating radar according to claim 3 is characterized in that: The calibration, denoising and smoothing of the radar data of the asphalt road section includes calibration and denoising according to the following formula: ； is the attenuation coefficient, is the ground penetrating radar antenna height, is the noise derived from the calibration data; after calibration and denoising, the grayscale value of the image is obtained by mapping the radar data of the asphalt road section according to the following formula: : ； In the formula, is the scaling factor, we have: ； In the formula, is the standard deviation of the signal; using a moving average filter, the signal strength after smoothing is expressed as: ； In the formula, is the window size of the moving average filter, Indicates at location The signal strength before smoothing.

6. The intelligent asphalt thickness detection method based on ground penetrating radar according to claim 3 is characterized by: The method of tracking and generating the optimal dividing line position by the dynamic programming algorithm includes: manually marking three anchor points on the dividing line, the x-axis is the horizontal scanning direction, and the range is ; The y-axis is the depth direction, and the x-axis coordinates of the three anchor points must satisfy: ； In the formula, , , The horizontal coordinates of the left anchor point, the middle anchor point, and the right anchor point are respectively; manually select the interface range under the asphalt layer in the area that meets the x-axis, and select the points in the local window that meet the signal stability and phase continuity: ； In the formula, represents the signal stability constraint, represents the mean value, A represents the signal amplitude, Represents the mean of the square of the signal amplitude in the entire area, Indicated by point As the center, the x-axis direction is from arrive , the y-axis direction is from arrive The signal amplitude matrix in the rectangular area of ​​; represents the phase continuity constraint, Indicates the sampling point index in the y-axis direction. Indicates at point The phase value at Indicates at point The phase value at the point where the constraint is satisfied; among the points where the signal amplitude in each x-axis partition is selected The largest point is used as the anchor point. Indicates at point The signal amplitude at ; Calculate the cost functions of the dividing line cost, smoothness cost and distance cost, and combine them to get a comprehensive cost function. For each column, it is necessary to calculate the optimal dividing line position of the current column based on the state of the previous column and the comprehensive cost function of the current column; when the last column is calculated, the termination condition is to find the minimum cost of the column. After obtaining the optimal dividing line position of the column, backtrack from the last column to the first column to find the optimal dividing line position of each column.

7. The intelligent asphalt thickness detection method based on ground penetrating radar according to claim 3 is characterized by: The calculation of the pavement asphalt thickness distribution is based on the following formula: ； In the formula, is the position of the dividing line in the B-scan image, is the corresponding time point, for depth; is the propagation speed of electromagnetic wave signal in asphalt, Satisfy the formula: ； In the formula, is the speed of light, is the relative dielectric constant of asphalt.

8. An intelligent asphalt thickness detection device based on ground penetrating radar, characterized in that: Suitable for executing any one of claims 1-7 of the intelligent asphalt thickness detection method based on ground penetrating radar.

9. The intelligent asphalt thickness detection device based on ground penetrating radar according to claim 8 is characterized in that: include: A multi-channel ground penetrating radar detection module, a lifting module, a power module and a control module. The multi-channel ground penetrating radar detection module includes a ground penetrating radar, an RTK positioning module and a wheel odometer.

10. An intelligent asphalt thickness detection system based on ground penetrating radar, characterized in that: A program module corresponding to the intelligent asphalt thickness detection method based on ground penetrating radar as claimed in any one of claims 1 to 7 includes: Data calibration module: used to make the multi-channel ground penetrating radar detection module perform periodic lifting and lowering movements to obtain calibration data and establish a noise signal model; Relative dielectric constant measurement module: used for single-point measurement and using the concentric method to measure the relative dielectric constant of the asphalt pavement to be tested; Asphalt road section radar data acquisition module: used to adjust the height of the ground penetrating radar antenna of the multi-channel ground penetrating radar detection module based on the relative dielectric constant of the asphalt road surface, and obtain the asphalt road section radar data continuously measured by the uniform translation multi-channel ground penetrating radar detection module; Data processing module: used to calibrate, denoise and smooth the radar data of asphalt road sections based on calibration data and noise signal models, track and generate the optimal dividing line position through dynamic programming algorithm, and calculate the distribution of asphalt thickness of the road surface.

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