An intelligent detection method, device and system for asphalt thickness based on ground penetrating radar

Abstract

The present invention provides an intelligent detection method, device and system for asphalt thickness based on ground penetrating radar, which relates to the technical field of asphalt thickness detection. It includes making a multi-channel ground penetrating radar detection module perform periodic lifting motion to obtain calibration data and establish a noise signal model; performing single-point measurement and measuring the relative dielectric constant of the asphalt pavement to be measured by the common center method; adjusting the height of the ground penetrating radar antenna of the multi-channel ground penetrating radar detection module and continuously measuring the radar data of the asphalt section; calibrating, denoising and smoothing the radar data of the asphalt section based on the calibration data and the noise signal model, tracking and generating the optimal boundary line position through the dynamic programming algorithm, and calculating the asphalt thickness distribution of the road surface. This application works in cooperation with a multi-channel ground penetrating radar and a lifting device, measures the relative dielectric constant by the common center point method, calculates the optimal height of the ground penetrating radar antenna, and uses the dynamic programming algorithm to track and generate the optimal boundary line 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:

[0003] (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, including the following steps: a. Determining the radar initial asphalt thickness value T at the target location based on n test channels; 0 , where n≥1; b. Based on the measured thickness value T at the target location and the initial asphalt thickness value T of the radar 0 Calibration is performed to determine a calibrated wave velocity c at a target location, and a real-time thickness H of asphalt paving is determined based at least on the calibrated wave velocity c at the target location. i .

[0004] (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.

[0005] 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

[0006] To solve the above problems, the technical solution adopted in this application is an intelligent detection method for asphalt thickness based on ground penetrating radar, including the following steps:

[0007] Obtain calibration data: 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;

[0008] Measure the relative dielectric constant: Move the multi-channel ground penetrating radar detection module, select measurement points at equal intervals, measure each point individually and use the common center method to measure the relative dielectric constant of the asphalt pavement to be measured;

[0009] Obtain radar data of the asphalt section: Based on the relative dielectric constant of the asphalt pavement, adjust the height of the ground penetrating radar antenna of the multi-channel ground penetrating radar detection module, and continuously measure the radar data of the asphalt section by translating the multi-channel ground penetrating radar detection module at a uniform speed;

[0010] Data processing: Calibrate, denoise, and smooth the radar data of the asphalt section based on the calibration data and the noise signal model, track and generate the position of the optimal dividing line through the dynamic programming algorithm, and calculate the asphalt thickness distribution of the road surface.

[0011] Optionally, obtaining calibration data includes collecting the radar signal intensity and the height of the ground penetrating radar antenna 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:

[0012] ;

[0013] In the formula, represents the radar signal intensity after attenuation at time , represents the radar signal intensity at the reference height, represents the attenuation coefficient, represents the time and the height of the ground penetrating radar antenna at time

[0014] The noise signal model is established as:

[0015] ;

[0016] In the formula, is the noise, is the amplitude of the noise, is the frequency of the noise, represents the time, represents the time and the height of the ground penetrating radar antenna at time

[0017] Optionally, measuring the relative dielectric constant of the asphalt pavement to be measured using the common center method includes using a multi-channel ground penetrating radar. Each time a test is performed, one transmitting channel is enabled to transmit an electromagnetic wave signal, and the first and last receiving channels are enabled to receive the electromagnetic wave signal. According to the time when the reflected wave from the lower interface of the asphalt layer is received by the two receiving channels, and the lengths of the transmitting antenna from the two receiving antennas, the relative dielectric constant of the asphalt pavement is calculated. At each measurement point, multiple transmitting channels are used for multiple measurements, and finally, the average value of the relative dielectric constants of the asphalt pavement measured at all measurement points and multiple transmitting channels at each measurement point is taken as the relative dielectric constant of the asphalt pavement to be measured.

[0018] Optionally, the calculation of the height of the multi-channel ground penetrating radar detection module from the ground includes making the antenna height satisfy Equation 1:

[0019] ;

[0020] In Equation 1, is the center frequency of the electromagnetic wave transmitted by the ground penetrating radar, is the speed of light, is the relative dielectric constant of the asphalt pavement to be measured;

[0021] And it is necessary to satisfy Equation 2:

[0022] ;

[0023] In Equation 2, is the thickness of the asphalt layer, Take the smallest integer that satisfies Equation 1 .

[0024] Optionally, calibrating, denoising, and smoothing the radar data of the asphalt section includes calibrating and denoising according to the following formula:

[0025] ;

[0026] is the attenuation coefficient, is the height of the ground penetrating radar antenna, is the noise derived from the calibration data; after calibration and denoising, the radar data of the asphalt section is mapped according to the following formula to obtain the image gray value :

[0027] ;

[0028] In the formula, is the scaling factor, and there is:

[0029] ;

[0030] In the formula, is the standard deviation of the signal; using a moving average filter, the smoothed signal intensity is expressed as:

[0031] ;

[0032] In the formula, is the window size of the moving average filter, represents the signal intensity before smoothing at position .

[0033] Optionally, tracking to generate the optimal demarcation line position through a dynamic programming algorithm includes: manually marking three anchor points on the demarcation line, with the x-axis being 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 need to satisfy:

[0034] ;

[0035] In the formula, , , are the abscissas of the left anchor point, the middle anchor point, and the right anchor point respectively; manually select the range of the lower interface of the asphalt layer in the area that satisfies the x-axis, and screen the points that satisfy signal stability and phase continuity in the local window:

[0036] ;

[0037] In the formula, represents the signal stability constraint, represents taking the mean value, A represents the signal amplitude, represents the mean value of the square of the signal amplitude in the entire area, represents taking the point as the center, from to in the x-axis direction, and from to in the y-axis direction of the signal amplitude matrix in the rectangular area; represents the phase continuity constraint, represents the sampling point index in the y-axis direction, represents the phase value at the point ; represents the phase value at the point ; among the points that satisfy the constraints, select the point with the largest signal amplitude in each x-axis partition as the anchor point, represents the signal amplitude at the point ;

[0038] The cost functions for calculating the boundary line cost, smoothness cost, and distance cost are combined to obtain a comprehensive cost function. For each column, the optimal boundary line position of the current column needs to be calculated based on the state of the previous column and the comprehensive cost function of the current column. When calculating to the last column, the termination condition is to find the minimum cost of that column. After obtaining the optimal boundary line position of that column, backtracking is used to find the optimal boundary line position of each column from the last column to the first column.

[0039] Optionally, the calculation of the asphalt thickness distribution of the road surface is based on the following formula:

[0040] ;

[0041] In the formula, is the position of the boundary line in the B-scan image, is the corresponding time point, is the depth; is the propagation speed of the electromagnetic wave signal in the asphalt, satisfies the formula:

[0042] ;

[0043] In the formula, is the speed of light, is the relative dielectric constant of the asphalt.

[0044] This application also provides an intelligent detection device for asphalt thickness based on ground penetrating radar, which is suitable for performing the intelligent detection method for asphalt thickness based on ground penetrating radar as described in any one of the foregoing.

[0045] Optionally, it includes: 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.

[0046] This application also provides an intelligent detection system for asphalt thickness based on ground penetrating radar, which has program modules corresponding to the intelligent detection method for asphalt thickness based on ground penetrating radar as described in any one of the foregoing, including:

[0047] Data calibration module: used to make the multi-channel ground penetrating radar detection module perform periodic lifting motion to obtain calibration data and establish a noise signal model;

[0048] Relative dielectric constant measurement module: used to perform single-point measurement and measure the relative dielectric constant of the asphalt pavement to be measured by the common center method;

[0049] Radar data acquisition module for asphalt section: 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 pavement, and acquire the radar data of the asphalt section continuously measured by translating the multi-channel ground penetrating radar detection module at a uniform speed;

[0050] Data processing module: It is used to calibrate, denoise and smooth the radar data of the asphalt road section based on the calibration data and the noise signal model, track and generate the optimal boundary line position through the dynamic programming algorithm, and calculate the pavement asphalt thickness distribution.

[0051] The beneficial effects of an intelligent asphalt thickness detection method, device and system based on ground penetrating radar provided by this application are as follows:

[0052] This application uses the multi-channel ground penetrating radar detection module and the lifting module to work together. By controlling the periodic lifting of the lifting device, the preliminary calibration data is collected and recorded for subsequent data processing of the formal detection data; combined with the working characteristics of the multi-channel ground penetrating radar detection module, the common midpoint method is used to measure the relative dielectric constant of the asphalt layer, calculate the propagation speed of electromagnetic waves in the asphalt layer, and provide a basis for calculating the optimal height of the ground penetrating radar antenna from the ground and the thickness of the asphalt layer. This invention automatically processes the ground penetrating radar data by tracking and generating the optimal boundary line position through the dynamic programming algorithm. According to the cost function and boundary conditions, the boundary line of the lower interface of the asphalt layer is calibrated to obtain the propagation time of electromagnetic waves in the asphalt layer, and then the thickness distribution of the asphalt layer is calculated. Brief Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of this application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art.

[0054] Figure 1 It is a three-dimensional schematic diagram of the structure of an intelligent asphalt thickness detection device based on ground penetrating radar in an embodiment of the present invention;

[0055] Figure 2 It is a left view of the structure of an intelligent asphalt thickness detection device based on ground penetrating radar in an embodiment of the present invention;

[0056] Figure 3 It is a schematic diagram of the arrangement of ground penetrating radar antennas in an embodiment of the present invention;

[0057] Figure 4 It is a schematic diagram of measuring the relative dielectric constant of the asphalt layer by the common midpoint method in an embodiment of the present invention;

[0058] Figure 5 It is a working flow chart of an intelligent asphalt thickness detection method based on ground penetrating radar in an embodiment of the present invention;

[0059] Figure 6 It is a data processing flow chart of an intelligent asphalt thickness detection method based on ground penetrating radar in an embodiment of the present invention.

[0060] Description of the drawing reference numerals: 11 - Handrail bracket; 12 - Connecting rod; 13 - Triangular bracket; 14 - Chassis bracket; 15 - Tire; 2 - Hydraulic lifting device; 3 - Control host; 4 - Battery; 51 - Ground penetrating radar; 511 - Power supply 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 course. Detailed implementation mode

[0061] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application clearer and more understandable, the present application will be further described in detail below with reference to the 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.

[0062] Embodiment 1

[0063] Combined with Figures 1-4 As shown, the present invention provides an intelligent asphalt thickness detection device based on ground penetrating radar, which includes 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.

[0064] 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 trolley bracket. The lifting module is the hydraulic lifting device 2, the control module is the control host 3, and the power module is the battery 4;

[0065] The trolley bracket includes a handrail bracket 11, a connecting rod 12, a triangular bracket 13, a chassis bracket 14 and a tire 15; the handrail bracket 11 supports the control host 3; the connecting rod 12 connects the handrail 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 provided with the hydraulic lifting device 2; the above brackets are all made of stainless steel, hollow inside, and the connection points are fixed by welding.

[0066] 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 cord, and the above cables are all embedded inside the bracket.

[0067] 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 a hydraulic system, enabling the ground penetrating radar 51 to lift and lower during the detection process, and recording the ground penetrating radar data under the condition of the change of the height of the ground penetrating radar antenna; secondly, it can flexibly adjust the working height of the ground penetrating radar according to different road conditions and detection requirements, ensuring that the ground penetrating radar antenna maintains the best detection distance from the asphalt road surface;

[0068] The control host 3 is used to configure the working parameters of the ground penetrating radar detection system 5, such as the trigger distance, time window, sampling points, etc., control the working mode of the ground penetrating radar and the start and stop of the detection, and perform data processing of the ground penetrating radar 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 in the data processing stage. The detection mode is to record the data of the asphalt section to be detected, and the single-point mode is to measure the relative dielectric constant of the single-point asphalt layer 9;

[0069] The multi-channel ground penetrating radar detection module includes a ground penetrating radar 51, an RTK positioning module 52, and a wheel type odometer 53. The ground penetrating radar 51 has four interfaces, namely a power supply 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 type odometer is installed at the central axis of one of the tires 15 and is connected to the odometer interface 514.

[0070] The ground penetrating radar detection system 5 uses the method of testing the road surface thickness with a short pulse radar to test the road surface thickness. The asphalt thickness is usually less than 10 cm. The antenna of the ground penetrating radar 51 is a replaceable module, and the optional range of the center frequency of the antenna is 1 GHz to 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, providing centimeter-level positioning coordinates for the system, used to record the position of the marked points and locate the detection route; the wheel type odometer 53 uses a 1024-sampling-point encoder and rotates coaxially with the tire center, serving as the trigger condition for the operation of the ground penetrating radar 51.

[0071] Embodiment 2

[0072] As Figures 3-5 shown, the present invention provides an intelligent detection method for asphalt thickness based on a ground penetrating radar.

[0073] 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, make the multi-channel ground penetrating radar detection module perform periodic lifting and lowering movements through the lifting module to obtain calibration data and establish a noise signal model;

[0074] The control host 3 controls the ground penetrating radar to switch to the calibration mode, installs the ground penetrating radar detection system 5 and the hydraulic lifting device 2 on the trolley bracket 1, and positions them above the asphalt pavement to be detected; starts the hydraulic lifting device 2 to keep the ground penetrating radar antenna 1 cm to 15 cm away from the ground and perform periodic slow lifting and lowering movements to record the ground penetrating radar calibration data.

[0075] The function of the hydraulic lifting device 2 is to adjust the distance between the ground penetrating radar and the asphalt pavement, thereby changing the propagation path of the radar wave. The periodic lifting and lowering movement in this process can ensure that the radar can receive different reflection signals from the asphalt pavement at multiple height positions. The movement control of the lifting device is realized through the hydraulic system, and its lifting amplitude and frequency are controlled to ensure that the ground penetrating radar reflects signals within a stable height range for multiple acquisitions; the height of the antenna changes during the calibration stage, and the radar signal strength will exponentially decay with the increase of the antenna height. The attenuation model is used to express the influence of height change on the signal amplitude:

[0076] ;

[0077] where, represents the radar signal strength after attenuation at time , represents the radar signal strength at the reference height. The reference height refers to selecting a known height, is the attenuation coefficient, which reflects the attenuation rate of the signal with height change, represents the height of the ground penetrating radar antenna at time ; by recording and the radar signal strength during the calibration stage, the attenuation coefficient can be estimated, and thus how to adjust the signal according to the fixed antenna height during the formal detection stage can be deduced;

[0078] Perform radar signal decomposition, use Fourier transform to obtain the frequency domain signal or wavelet transform to obtain the time-frequency domain signal. By analyzing the amplitude of the frequency domain signal or time-frequency domain signal, determine the amplitude of the noise signal, identify the main frequency components of the noise signal, and analyze the phase change of the noise signal.

[0079] For example, as a feasible implementation method, first perform a fast Fourier transform on the radar signal to obtain the spectrum, mark the signal peaks through threshold detection or peak search (such as 3 times the standard deviation higher than the average amplitude), and regard the remaining frequency bands as the noise-dominated regions. Identify the frequency component with the largest amplitude as the frequency of the noise. Calculate the average power spectral density (PSD) in the noise-dominated frequency band and convert it to the time-domain noise amplitude (RMS value).

[0080] Based on the aforementioned antenna height information, for the noise at each height, use sine fitting to extract the phase from the model where is the amplitude of the noise, is the frequency of the noise, summarize the phase values corresponding to different heights, and fit the functional form of the phase offset related to the height.

[0081] According to the calibration data, establish a noise signal model to obtain the relationship between the noise and the antenna height :

[0082] ;

[0083] where is the amplitude of the noise, is the frequency of the noise, is the phase offset related to the height; through the noise data in the calibration stage, estimate the noise characteristics at different heights, obtain the reflection intensity data at different propagation times as calibration data, and provide a basis for removing noise and filtering data from the actual detection data later;

[0084] Measure the relative dielectric constant: Move the multi-channel ground penetrating radar detection module, select measurement points at equal intervals, and measure the relative dielectric constant of the asphalt pavement to be measured at a single point and using the common center method;

[0085] Measuring the relative dielectric constant of the asphalt pavement by the common center point method specifically includes using a multi-channel ground penetrating radar. In this embodiment, there are 7 transmitting channels 7 and 8 receiving channels 6 in total. The transmitting channels 7 and the receiving channels 6 are both arranged horizontally; in measuring the relative dielectric constant of the asphalt pavement by the common center method, the control host (3) controls the working mode of the ground penetrating radar to be switched to the single point mode, only enabling one transmitting channel 7 to transmit electromagnetic wave signals, and enabling the first and last receiving channels 6 to receive electromagnetic wave signals. The electromagnetic wave propagation in the asphalt layer 9 above the base layer 10 satisfies the formula:

[0086] ;

[0087] where is the propagation speed of the electromagnetic wave in the asphalt layer 9, is the speed of light, is the relative dielectric constant of the asphalt, and according to the geometric relationship of the underground medium, it can be obtained that:

[0088] ;

[0089] where is the thickness of the asphalt layer 9, and are the times when the lower interface reflection waves of the asphalt layer 9 are received by the two receiving channels 6 respectively, and are the lengths from the transmitting antenna to the two receiving antennas respectively, and the following can be solved:

[0090] ;

[0091] Among them, is the relative dielectric constant of the asphalt layer 9 measured when using the first transmitting channel 7; the ground penetrating radar used in the present invention has a total of 7 transmitting channels 7 and 8 receiving channels 6. Signals are transmitted using 7 transmitting channels 7 respectively, and signals are received using the first and the eighth receiving channels 6. Then, when using the th transmitting channel 7, the measured relative dielectric constant is:

[0092] ;

[0093] In the formula, and respectively represent the times when the lower interface reflection waves of the asphalt layer 9 are received by the two receiving channels 6 when using the th transmitting channel 7, and respectively represent the lengths from the transmitting antenna to the two receiving antennas when using the th transmitting channel 7. The average value of the relative dielectric constants of the asphalt layer 9 measured by 7 channels at 5 measurement points is taken:

[0094] ;

[0095] Through the above steps, the relative dielectric constant of the asphalt layer 9 is deduced, providing 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;

[0096] Obtain radar data of the asphalt road section: Based on the relative dielectric constant of the asphalt pavement, use the lifting module to adjust the height of the ground penetrating radar antenna of the multi-channel ground penetrating radar detection module, and continuously measure the radar data of the asphalt road section by translating the multi-channel ground penetrating radar detection module at a constant speed;

[0097] Calculate and adjust the optimal height of the ground penetrating radar antenna from the ground, specifically 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, and reduce the near-field coupling noise, the antenna height needs to satisfy Equation 1:

[0098] ;

[0099] In the formula, is the center frequency of the electromagnetic wave emitted by the ground penetrating radar, is the speed of light, is the relative dielectric constant of asphalt calculated in claim 7;

[0100] To achieve beam focusing, so that the reflected wave of the ground penetrating radar electromagnetic wave at the lower interface of the asphalt layer 9 and the direct wave are in phase addition, the phase difference between the two waves is an odd multiple of half wavelength, and the antenna height needs to satisfy Equation 2:

[0101] ;

[0102] wherein, is the thickness of the asphalt layer, takes the smallest integer that satisfies Equation 1 , and the value of can be determined as:

[0103] ;

[0104] Step three: Continuously detect the asphalt pavement, specifically including: starting the ground penetrating radar system, and the control host (3) controls to switch the working mode of the ground penetrating radar to the detection mode; pushing the trolley support to move forward uniformly along the detection section, and the ground penetrating radar system will continuously emit electromagnetic waves and receive reflection signals; all the collected radar data, including the propagation time, reflection intensity of the reflection wave, 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;

[0105] Data processing: Based on the calibration data and the noise signal model, calibrate, denoise and smooth the radar data of the asphalt section, and the dynamic programming algorithm tracks to generate the optimal boundary position, and calculates the asphalt thickness distribution of the road surface.

[0106] As Figure 6 shown, the data processing of the ground penetrating radar data specifically includes: First, through the height and signal relationship model obtained in the calibration stage, calibrate and denoise the formal detection data, and the signal after calibration and denoising is:

[0107] ;

[0108] wherein, is the attenuation coefficient, is the calculated antenna height of the ground penetrating radar, is the noise derived from the calibration data;

[0109] After denoising the ground penetrating radar echo signal, the obtained signal is a time-domain signal, and it needs to be mapped to the gray value of the image ; Assume that in the B-scan image, each point corresponds to a time point , and the time-domain signal is mapped to the gray value of the image through logarithmic mapping:

[0110] ;

[0111] Among them, represents each point in the B-scan image corresponding time point, represents the time-domain signal intensity at the time point , corresponding to the image coordinate , is a scaling factor, which controls the gain of the signal after logarithmic transformation. By taking values from the dynamic range of the signal, strong signals can be compressed and weak signals can be enhanced:

[0112] ;

[0113] Among them, 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 is needed to compress the signal range;

[0114] In the calculation of the image gray value, the signal needs to be smoothed to reduce the influence of noise on the image quality. Using a moving average filter, the signal intensity after smoothing can be expressed as:

[0115] ;

[0116] In the formula, is the window size of the moving average filter, represents the signal intensity before smoothing at the position . In the detected B-scan image, three points on the manually marked boundary line are marked, and the dynamic programming algorithm is used to track and generate the complete boundary line.

[0117] The dynamic programming algorithm to track and generate the optimal boundary line position 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 need to satisfy:

[0118] ;

[0119] In the formula, , , ​They are the abscissas of the left anchor point, the middle anchor point, and the right anchor point respectively; manually frame the range of the lower interface of the asphalt layer 9 in the area that satisfies the x-axis, and screen the points that meet the signal stability and phase continuity in the local window:

[0120] ;

[0121] In the formula, represents the signal stability constraint, and the constraint is that the ratio of the local signal energy to the global energy with Δx = 2 and Δy = 5 is greater than 2, represents taking the mean value, represents the signal amplitude, represents the mean value of the squares of the signal amplitudes in the entire area, which is used as the benchmark of the global energy, represents taking the point as the center, and in the x-axis direction from to , and in the y-axis direction from to of the signal amplitude matrix within the rectangular area; represents the phase continuity constraint, and the constraint is that the cosine mean value of 5 consecutive sampling points in the y-axis direction is greater than 0.8, represents the sampling point index in the y-axis direction, which is used to traverse 5 consecutive sampling points, represents at the point the phase value, represents at the point the phase value; among the points that meet the constraints, select the point with the largest signal amplitude in each x-axis partition as the anchor point, represents at the point the signal amplitude;

[0122] The cost functions for calculating the boundary line cost, smoothness cost, and distance cost are combined to obtain the comprehensive cost function. For each column, the optimal boundary line position of the current column needs to be calculated based on the state of the previous column and the comprehensive cost function of the current column; when calculating to the last column, the termination condition is to find the minimum cost of this column. After obtaining the optimal boundary line position of this column, backtracking is performed from the last column to the first column to find the optimal boundary line position of each column.

[0123] Due to the differences in underground media, the phase of the reflected wave received during the propagation of electromagnetic waves changes. The boundary between media is reflected as a place with sudden gray level changes in the B-scan gray scale image. Part of the cost function in the dynamic programming algorithm reflects the change of the image gray level. Assume that is the gray level value at the position in the image, and the cost of the boundary line can be expressed as:

[0124] ;

[0125] Among them, and are the positions of the dividing lines between the previous column and the current column respectively, and are the grayscale values at the corresponding positions. The position of the mutation is usually the position of the dividing line;

[0126] To ensure that the dividing line does not change much between consecutive columns, the smoothness cost can be expressed as

[0127] ;

[0128] Among them, is the smoothing coefficient, which controls the smoothness of the dividing line;

[0129] The three points manually marked in the B-scan image affect the final dividing line. The cost function takes into account the position constraints of these marked points. The manually marked point is the true position of the dividing line. Then the cost function can include the distance cost from this point:

[0130] ;

[0131] Among them, is the constraint coefficient, which controls the influence of the manually marked points;

[0132] Combining the above three cost functions, the final cost function is obtained:

[0133] ;

[0134] 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 the initial conditions. Assuming that the position of the dividing line is marked at , the boundary conditions can be initialized as:

[0135] ;

[0136] For each column , it is necessary to calculate the optimal position of the dividing line for the current column according to the state of the previous column and the cost function of the current column. The recurrence formula is:

[0137] ;

[0138] Among them, is the minimum cost when the position of the dividing line is at in column represents the previous column At this point, the demarcation line is located at the position of the cumulative minimum cost when is the cost of migrating from the previous column position to the current column position;

[0139] When calculating to the last column, the termination condition is to find the minimum cost of this column, that is:

[0140] ;

[0141] In the formula, represents the index of the last column of the image, represents the possible position of the demarcation line at the last column to traverse all possible demarcation line positions in this column to find the optimal position with the minimum cumulative cost, represents the optimal demarcation line position at the last column .

[0142] After obtaining the optimal demarcation line position of this column, by backtracking from the last column to the first column, the optimal demarcation line position of each column is found. The backtracking process starts from the optimal position of the last column and gradually returns to the first column. When backtracking, select the path with the minimum cost and obtain the optimal demarcation line position of each column:

[0143] ;

[0144] In the formula, represents the optimal demarcation line position of each column. The thickness distribution of the asphalt layer 9 of the road surface to be detected specifically includes: the known position of the demarcation line in the B-scan image and the corresponding time point , the depth can be calculated:

[0145] ;

[0146] Among them, is the propagation speed of the electromagnetic wave signal in the medium, is the speed of light, is the relative dielectric constant of the asphalt layer 9, from which it can be deduced that:

[0147] ;

[0148] The thickness distribution of the asphalt layer 9 of the road surface to be detected is calculated.

[0149] Example 3

[0150] 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 ns, the number of sampling points is 128 points. The ground penetrating radar detection system 5 selects the center frequency of the antenna of the ground penetrating radar to be 1 GHz, and the wheel odometer 53 uses a 1024-sampling-point encoder;

[0151] Select the asphalt test section A, install the ground penetrating radar detection system, and the control host controls the working mode of the ground penetrating radar to be switched to the calibration mode, record the calibration data of the ground penetrating radar, and establish a signal attenuation model and a noise model ;

[0152] In the asphalt test section A, 5 measurement points are evenly selected and marked at equal intervals. The relative permittivity of the asphalt layer 9 is measured using the common midpoint method, and the electromagnetic wave propagation speed is calculated using the wave velocity formula. The example data of the first point are as follows:

[0153] Transmitting channel: Tx3 (the 3rd channel)

[0154] Receiving channels: Rx1 (the first channel) and Rx8 (the last channel)

[0155] Channel spacing: The horizontal spacing between Tx3 and Rx1 is 25 cm; the horizontal spacing between Tx3 and Rx8 is 35 cm

[0156] Measurement data: The time for the electromagnetic wave to reach Rx1 is 2 ns; the time for the electromagnetic wave to reach Rx8 is 3.2 ns

[0157] The relative permittivity calculated is:

[0158] ;

[0159] After calculation, the average relative permittivity of the asphalt layer 9 in the asphalt test section A is 4.0, and the electromagnetic wave velocity is calculated:

[0160] ;

[0161] Based on the relative permittivity of the asphalt layer 9, calculate and adjust the optimal height of the ground penetrating radar antenna from the ground, and calculate the lower limit of the antenna height:

[0162] ;

[0163] Calculate the antenna height that satisfies the beam focusing condition, taking n = 1:

[0164] ;

[0165] Verify that 12.5 mm < 23.9 mm, which does not meet the condition; take n = 2:

[0166] ;

[0167] Verify that 37.5mm > 23.9mm. If the condition is met, then take to be 37.5mm;

[0168] Push the trolley to move forward at a constant speed, start the ground penetrating radar detection system, and conduct continuous detection along the asphalt pavement to be detected. Record and store the ground penetrating radar data of the asphalt section to be detected, which will be used as the original data of this section;

[0169] Perform data processing on the collected ground penetrating radar data. According to the calibration data , conduct filtering and enhancement processing on the data. First, through the radar height and the attenuation coefficient obtained through experiments , through the formula , perform dynamic compensation on the original signal; Subsequently, use logarithmic mapping to convert the time-domain signal into a grayscale image, where the scaling factor is adaptively adjusted according to the signal standard deviation. Finally, use a moving average filter to smooth the image. The window size N is preferably 5 - 15 to suppress noise and improve the thickness inversion accuracy.

[0170] Next, adopt the boundary tracking algorithm of dynamic programming to calibrate the lower interface of the ground 8 and the asphalt layer 9, obtain the propagation time of electromagnetic waves in the asphalt layer 9, and finally calculate the thickness distribution of the asphalt layer 9 of the pavement to be detected. The steps are as follows:

[0171] (1) Manually mark the left endpoint, the middle reference point, and the right endpoint at 1 / 16, 1 / 2, and 15 / 16 of the B-scan image respectively;

[0172] (2) Construct a comprehensive cost function , including the cost of gray-scale mutation , the smoothness cost (smoothness coefficient λ = 0.5 - 1.5) and the constraint cost (constraint coefficient μ = 0.3 - 0.8);

[0173] (3) Calculate the minimum cumulative cost column by column from left to right, allowing the vertical coordinate offset of adjacent columns pixels;

[0174] (4) Backtrack to generate the optimal boundary;

[0175] (5) According to the boundary position and the wave speed of electromagnetic waves in the asphalt layer 9, calculate the thickness of the asphalt layer 9;

[0176] Example 4

[0177] 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 ns, the number of sampling points is 128, the ground penetrating radar detection system 5 selects the center frequency of the antenna of the ground penetrating radar to be 2 GHz, and the wheel odometer 53 uses a 1024-sampling-point encoder;

[0178] Select asphalt test section B, install the ground penetrating radar detection system, the control host controls the working mode of the ground penetrating radar to be switched to the calibration mode, record the calibration data of the ground penetrating radar, and establish a signal attenuation model and a noise model ;

[0179] In asphalt test section B, 5 measurement points are evenly selected and marked at intervals, and the relative dielectric constant of the asphalt layer 9 is measured using the common midpoint method, and the electromagnetic wave propagation speed is calculated using the wave velocity formula. The example data of the first point are:

[0180] Transmitting channel: Tx2 (the second channel)

[0181] Receiving channels: Rx1 (the first channel) and Rx8 (the last channel)

[0182] Channel spacing: Horizontal spacing between Tx2 and Rx1: 20 cm; Horizontal spacing between Tx2 and Rx8: 50 cm

[0183] Measurement data: The time for the electromagnetic wave to reach Rx1 is 1.5 ns; The time for the electromagnetic wave to reach Rx8 is 3.2 ns

[0184] The relative dielectric constant calculated is:

[0185] ;

[0186] After calculation, the average relative dielectric constant of the asphalt layer 9 in asphalt test section A is 4.0, and the electromagnetic wave velocity is calculated:

[0187] ;

[0188] Based on the relative dielectric constant of the asphalt layer 9, calculate and adjust the optimal height of the ground penetrating radar antenna from the ground, and calculate the lower limit of the antenna height:

[0189] ;

[0190] Calculate the antenna height that satisfies the beam focusing condition, substitute h = 0.06 m, and take n = 1:

[0191] ;

[0192] Verify that 16.04 mm > 12.7 mm, which meets the condition, so take as 16 mm;

[0193] Push the trolley to move forward at a constant speed, start the ground penetrating radar detection system, and conduct continuous detection along the asphalt pavement to be detected. Record and store the ground penetrating radar data of the asphalt section to be detected, which will be used as the original data of this section.

[0194] Perform data processing on the collected ground penetrating radar data, and according to the calibration data , conduct filtering and enhancement processing of the data. First, through the radar height and the attenuation coefficient obtained through experiments , perform dynamic compensation on the original signal through the formula . Subsequently, use the logarithmic mapping to convert the time-domain signal into a grayscale image, where the scaling factor is adaptively adjusted according to the signal standard deviation. Finally, use a moving average filter to smooth the image. The window size N is preferably 5 - 15 to suppress noise and improve the thickness inversion accuracy.

[0195] Next, adopt the dynamic programming boundary tracking algorithm to calibrate the lower interface of 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 pavement to be detected. The steps are as follows:

[0196] (1) Manually mark the left endpoint, the middle reference point, and the right endpoint at 1 / 16, 1 / 2, and 15 / 16 of the B-scan image respectively;

[0197] (2) Construct a comprehensive cost function , including the cost of gray level mutation , the smoothness cost (smooth coefficient λ = 0.5 - 1.5) and the constraint cost (constraint coefficient μ = 0.3 - 0.8);

[0198] (3) Calculate the minimum cumulative cost column by column from left to right, allowing the vertical offset of adjacent columns pixels;

[0199] (4) Backtrack to generate the optimal boundary;

[0200] (5) Calculate the thickness of the asphalt layer 9 according to the boundary position and the wave velocity of the electromagnetic wave in the asphalt layer 9;

[0201] Performance verification:

[0202] To verify the effectiveness of the dynamic programming boundary method, select a detection scenario of the asphalt layer 9 on a certain highway for a comparative experiment:

[0203] Test conditions: Dataset: 100 groups of B-scan images (including manually labeled true demarcation lines);

[0204] Comparison methods: Traditional Canny edge detection method, fixed threshold segmentation method;

[0205] Parameter settings: Smoothing coefficient λ = 0.8, constraint coefficient μ = 0.5, window offset Δ = 5 pixels.

[0206] Test results: Positioning accuracy: The average error of the demarcation line of the method of the present invention is 1.2 mm (4.5 mm for the traditional method), and the accuracy is improved by 73%;

[0207] Anti-noise ability: When the signal-to-noise ratio ≥ 15 dB, the error rate is stable within 2% (the error rate of the traditional method > 10%);

[0208] Computing efficiency: The processing time of a single-frame image is ≤ 0.8 seconds (≥ 1.5 seconds for the traditional method).

[0209] Conclusion: The present invention significantly improves the positioning accuracy and speed of the demarcation line through the dynamic programming algorithm combined with manual annotation constraints.

[0210] Example 5

[0211] A ground-penetrating radar-based asphalt thickness detection method proposed by the present invention is the underlying technical core of the present invention, and various products can be derived based on the algorithm.

[0212] Based on the method proposed by the present invention, an intelligent ground-penetrating radar-based asphalt thickness detection system is developed using a programming language. The system has program modules corresponding to the steps of the above technical solution, and executes the steps in the above ground-penetrating radar-based asphalt thickness method when running, at least including:

[0213] 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;

[0214] Relative dielectric constant measurement module: Used for single-point measurement and measuring the relative dielectric constant of the asphalt pavement to be measured using the common center method;

[0215] Radar data acquisition module for asphalt section: 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 pavement, and obtain the radar data of the asphalt section continuously measured by moving the multi-channel ground-penetrating radar detection module at a uniform speed;

[0216] Data processing module: Used to calibrate, denoise, and smooth the radar data of the asphalt section based on the calibration data and the noise signal model, track and generate the optimal demarcation line position through the dynamic programming algorithm, and calculate the asphalt thickness distribution of the road surface.

[0217] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included within 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: 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 algorithms, and calculate the distribution of asphalt thickness of the road surface; 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 , .

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 method for intelligent detection of asphalt thickness 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: ； Indicates at time At time , the radar signal strength after attenuation is 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.

5. The intelligent asphalt thickness detection method based on ground penetrating radar according to claim 3 is characterized in that: 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.

6. 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 the asphalt pavement to be tested.

7. An intelligent asphalt thickness detection device based on ground penetrating radar, characterized in that: Suitable for executing any one of claims 1-6 of the intelligent asphalt thickness detection method based on ground penetrating radar.

8. The intelligent asphalt thickness detection device based on ground penetrating radar according to claim 7 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.

9. 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 6 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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