A method and device for automatically locating steel bars based on ground penetrating radar

Abstract

The present invention discloses a method and device for automatic steel bar positioning based on ground penetrating radar. The present invention does not require human intervention and can significantly improve data processing efficiency. At the same time, before positioning, the data is corrected and denoised, so that the interference of noise can be reduced, thereby ensuring the accuracy of the results. Furthermore, during positioning, signal enhancement and offset processing are introduced, based on which the profile data of the steel bar signal can be highlighted, thereby facilitating subsequent steel bar identification and analysis. Finally, when performing size detection, the shape characteristics of the steel bar are comprehensively analyzed to retain the real steel bar area, and based on the real steel bar area, size detection, protective layer thickness detection and horizontal spacing measurement are performed. In this way, the interference area can be further filtered out, thereby realizing the accurate measurement of steel bar parameters. Based on the above explanation, the present invention can greatly improve the efficiency and accuracy of steel bar detection in concrete structures.

Description

Technical Field

[0001] The present invention belongs to the technical field of steel bar positioning and processing, and in particular relates to a steel bar automatic positioning method and device based on ground penetrating radar. Background Art

[0002] In key infrastructure areas such as water conservancy, railways, and highways, the structural integrity of concrete linings is a core element in ensuring project safety. Accurate determination of parameters such as the position of steel bars, protective layer thickness, and spacing is crucial to ensuring the long-term stability and functionality of the structure. Therefore, precise positioning of steel bars is an important reference for evaluating whether steel bar construction is qualified and standardized, and has become a vital part of ensuring the safety of concrete lining structures.

[0003] Currently, high-frequency ground-penetrating radar (GPR) technology, with its advantages of non-destructiveness and high resolution, has been widely used to detect and locate rebar in concrete structures. However, GPR faces significant data processing challenges in practical applications. The amount of GPR data collected on-site is huge and the data features are complex. Traditional manual identification and recording methods are no longer able to meet the current requirements for work efficiency and detection accuracy. At the same time, the technical difficulties of automatically extracting rebar quantity and location information from GPR scanning profiles have not been fully resolved. Automated processing requires overcoming multiple technical difficulties, such as suppressing noise interference, accurately identifying and analyzing rebar signals, and accurately measuring the thickness of rebar cover. Therefore, the automatic location of rebar based on high-frequency GPR technology still faces certain application difficulties. Therefore, based on the aforementioned shortcomings, how to provide a high-efficiency and high-accuracy automatic rebar location method based on GPR has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and device for automatic positioning of steel bars based on ground penetrating radar, so as to solve the problems of low efficiency and poor accuracy in the existing technology of manual identification and recording, as well as the inability to overcome various technical difficulties in the use of automated processing.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, a method for automatically locating steel bars based on ground penetrating radar is provided, comprising:

[0007] Acquiring ground-penetrating radar profile data of the concrete to be tested, and performing data preprocessing on the ground-penetrating radar profile data to obtain preprocessed ground-penetrating radar profile data, wherein the data preprocessing includes data correction processing and data denoising processing;

[0008] Performing reinforcement reflection signal enhancement processing on the pre-processed ground penetrating radar profile data to obtain enhanced profile data, and performing data offset processing on the enhanced profile data to obtain offset profile data;

[0009] performing steel bar identification processing on the offset profile data to obtain at least one steel bar initial region in the ground penetrating radar profile data;

[0010] Performing shape feature extraction processing on each steel bar initial region in the at least one steel bar initial region to obtain steel bar shape features of each steel bar initial region;

[0011] determining at least one steel bar reference region in the ground penetrating radar profile data based on the shape characteristics of each steel bar;

[0012] Determining the size information of each steel bar in the concrete to be tested using at least one steel bar reference area;

[0013] According to the size information of each steel bar, the protective layer thickness and the horizontal spacing of each steel bar in the concrete to be tested are obtained, so as to complete the automatic positioning processing of the steel bars in the concrete to be tested after the protective layer thickness and the horizontal spacing of the steel bars are obtained.

[0014] Based on the above disclosure, after obtaining the GPR profile data of the concrete to be tested, the present invention first performs correction and denoising processing on the data to achieve data alignment of the GPR profile data and removal of interference data, thereby ensuring data accuracy; after completing data preprocessing, the preprocessed data can be subjected to steel bar reflection signal enhancement processing, thereby enhancing the steel bar reflection signal and suppressing the background and smaller structural reflections in the image; then, the enhanced profile data is subjected to data offset processing, and the steel bar reflection signal can be returned to its original position, that is, the hyperbola is converged to highlight the effective signal; in this way, the aforementioned signal enhancement and offset processing can facilitate the subsequent accurate identification and analysis of steel bar signals; then, the present invention performs steel bar identification processing on the offset profile data to obtain at least one steel bar initial area; then, the steel bar shape features of each steel bar initial area are extracted, and based on this, the reference area of ​​the real steel bar is determined; finally, the size information of each steel bar is obtained based on the reference area of ​​the real steel bar, and based on the size information, the protective layer thickness and horizontal spacing of the steel bar can be obtained.

[0015] Through the above design, the automatic steel bar positioning method provided by the present invention does not require human participation, can significantly improve data processing efficiency, and can greatly reduce engineering time and cost; at the same time, before positioning, the data is corrected and denoised, so that the interference of noise can be reduced, thereby ensuring the accuracy of the results; further, during positioning, signal enhancement and offset processing are introduced, based on which the profile data of the steel bar signal can be highlighted, thereby facilitating subsequent steel bar identification and analysis; finally, when performing size detection, the shape characteristics of the steel bar are comprehensively analyzed to judge and retain the reference area of ​​the real steel bar, and based on this, size detection, protective layer thickness detection and horizontal spacing measurement are performed; thereby, the interference area can be further filtered out, thereby realizing the accurate measurement of steel bar parameters; based on the foregoing explanation, the present invention provides an innovative solution for ground penetrating radar data processing, introducing a fully automated, high-precision steel bar positioning and parameter calibration technology, which can greatly improve the efficiency and accuracy of steel bar detection in concrete structures; therefore, it is very suitable for large-scale application and promotion.

[0016] In a possible design, the pre-processed GPR profile data is subjected to reinforcement reflection signal enhancement processing to obtain enhanced profile data, including:

[0017] Performing average filtering on the pre-processed ground penetrating radar profile data to obtain filtered ground penetrating radar profile data;

[0018] Performing differential enhancement processing on the filtered ground penetrating radar profile data to obtain differential enhanced ground penetrating radar profile data;

[0019] Performing nonlinear transformation on the differential enhanced ground penetrating radar profile data to obtain transformed ground penetrating radar profile data;

[0020] performing invalid signal removal processing on the converted ground penetrating radar profile data, so as to obtain valid ground penetrating radar profile data after the invalid signal removal processing;

[0021] Data reconstruction processing is performed on the effective ground penetrating radar profile data to obtain enhanced profile data after the data reconstruction processing.

[0022] In a possible design, the differential enhanced GPR profile data is subjected to nonlinear transformation processing to obtain transformed GPR profile data, including:

[0023] The differential enhanced ground penetrating radar profile data is subjected to nonlinear transformation processing using the following formula (1) to obtain transformed ground penetrating radar profile data;

[0024] E(t)=α·log(1+β·|D(t)|) (1)

[0025] In the above formula (1), D(t) represents the differential enhanced ground penetrating radar profile data, E(t) represents the converted ground penetrating radar profile data, and α and β both represent nonlinear adjustment factors;

[0026] Accordingly, performing invalid signal removal processing on the converted ground penetrating radar profile data to obtain valid ground penetrating radar profile data after the invalid signal removal processing includes:

[0027] Calculating the mean and standard deviation of the converted ground penetrating radar profile data;

[0028] According to the mean value and the standard deviation, the signal threshold is calculated using the following formula (2);

[0029] θ=μ+k′·σ (2)

[0030] In the above formula (2), θ represents the signal threshold, μ represents the average value, σ represents the standard deviation, and k′ represents the adjustment parameter;

[0031] According to the signal threshold, invalid signal removal processing is performed on the converted ground penetrating radar profile data to obtain the valid ground penetrating radar profile data after the invalid signal removal processing.

[0032] In one possible design, performing steel bar identification processing on the offset profile data to obtain at least one steel bar initial region in the ground penetrating radar profile data includes:

[0033] For any pixel point in the offset profile data, obtaining a neighborhood area of ​​the any pixel point;

[0034] Calculate the average brightness and brightness standard deviation of all neighboring pixels in the neighborhood area;

[0035] performing a local contrast enhancement process on the any pixel point according to the average brightness and the brightness standard deviation, so as to obtain the any pixel point after the local contrast enhancement process, and obtaining the locally contrast enhanced profile data after polling all pixel points in the offset profile data;

[0036] The section data after local contrast enhancement is subjected to steel bar identification processing, so as to obtain at least one steel bar initial region in the ground penetrating radar section data after the steel bar identification processing.

[0037] In one possible design, performing local contrast enhancement processing on any pixel point according to the average brightness and the brightness standard deviation includes:

[0038] The following formula (3) is used to perform local contrast enhancement processing on any pixel point;

[0039] E(x,y)=I(x,y)+γσlocal(x,y)·[I(x,y)-μlocal(x,y)] (3)

[0040] In the above formula (3), I(x,y) represents the brightness of any pixel, σlocal(x,y) represents the brightness standard deviation, μlocal(x,y) represents the average brightness, γ represents the contrast adjustment factor, and E(x,y) represents the brightness of any pixel after local contrast enhancement;

[0041] Accordingly, performing steel bar identification processing on the locally contrast-enhanced profile data to obtain at least one steel bar initial region in the ground penetrating radar profile data after the steel bar identification processing includes:

[0042] Screening out steel bar pixels from the locally contrast enhanced cross-sectional data according to a brightness value of each pixel in the locally contrast enhanced cross-sectional data and a local average brightness of each pixel, wherein the local average brightness of each pixel is an average brightness of a neighborhood area of ​​each pixel before the local contrast enhancement is performed;

[0043] At least one initial steel bar region in the ground penetrating radar profile data is determined using the screened steel bar pixel points.

[0044] In one possible design, performing shape feature extraction processing on each of the at least one steel bar initial region to obtain steel bar shape features of each steel bar initial region includes:

[0045] For any initial area of ​​a steel bar, calculate the area and perimeter of the initial area of ​​the steel bar;

[0046] According to the area and perimeter of the initial region of any steel bar, and in accordance with the following formula (4), the shape complexity of the initial region of any steel bar is calculated;

[0047]

[0048] In the above formula (4), F represents the shape complexity of the initial region of any steel bar, P represents the perimeter of the initial region of any steel bar, and A represents the area of ​​the initial region of any steel bar;

[0049] Using the shape complexity of any one of the steel bar initial regions as the steel bar shape feature of the any one of the steel bar initial regions;

[0050] Accordingly, determining at least one steel bar reference area in the GPR profile data based on the shape characteristics of each steel bar includes:

[0051] From the at least one steel bar initial region, a steel bar initial region having a shape complexity less than or equal to a complexity threshold is screened out as a steel bar reference region.

[0052] In one possible design, at least one steel bar reference region is used to determine the size information of each steel bar in the concrete to be tested, including:

[0053] For any one of the at least one steel bar reference area, obtaining a set of boundary pixel points of the any one steel bar reference area;

[0054] Using the boundary pixel point set and according to the following formula (5), a size reconstruction function of the steel bar corresponding to any steel bar reference area is constructed;

[0055]

[0056] In the above formula (5), E(x c ,y c , r) represents the size reconstruction function, x c ,y c represents the horizontal and vertical coordinates of the center of the steel bar corresponding to any steel bar reference area in the ground penetrating radar section, r represents the radius of the steel bar corresponding to any steel bar reference area, x represents the horizontal and vertical coordinates of the center of the steel bar corresponding to any steel bar reference area in the ground penetrating radar section, r represents the radius of the steel bar corresponding to any steel bar reference area, m ,y m represents the horizontal coordinate and vertical coordinate of the mth boundary pixel point in the boundary pixel point set, and n represents the total number of boundary pixel points;

[0057] Minimizing the size reconstruction function to obtain the fitting center coordinates and fitting radius of the steel bar corresponding to any steel bar reference area after minimizing the size reconstruction function;

[0058] According to the fitting center coordinates and the fitting radius, the actual center coordinates and actual radius of the steel bars corresponding to any steel bar reference area are obtained, so as to use the actual center coordinates and actual radius of the steel bars corresponding to any steel bar reference area to form the size information of the steel bars corresponding to any steel bar reference area.

[0059] In a possible design, the dimensional information of any steel bar includes the actual center coordinates and actual radius of the steel bar. The cover thickness and horizontal spacing of each steel bar in the concrete to be tested are obtained based on the dimensional information of each steel bar, including:

[0060] Determining the vertex coordinates of any one of the steel bars according to the actual center coordinates and actual radius of any one of the steel bars;

[0061] According to the vertex coordinates of any one of the steel bars and in accordance with the following formula (6), the protective layer thickness of any one of the steel bars is calculated;

[0062]

[0063] In the above formula (6), h represents the thickness of the protective layer of any steel bar, y d represents the vertical coordinate of the vertex coordinate of any steel bar, T represents the total sampling time of the ground penetrating radar profile data, N represents the number of sampling points of the ground penetrating radar profile data, ε r Indicates the dielectric constant of the concrete to be tested;

[0064] Obtaining vertex coordinates of a steel bar adjacent to any one of the steel bars in the concrete to be tested;

[0065] The horizontal spacing between any one steel bar and the adjacent steel bar is calculated according to the horizontal coordinate of the vertex coordinates of any one steel bar and the horizontal coordinate of the vertex coordinates of the steel bar adjacent to the any one steel bar.

[0066] In one possible design, performing data preprocessing on the ground penetrating radar profile data to obtain preprocessed ground penetrating radar profile data includes:

[0067] Using a zero-point correction algorithm, the ground-penetrating radar profile data is corrected to obtain corrected ground-penetrating radar profile data;

[0068] The corrected ground penetrating radar profile data is subjected to data denoising processing to obtain the pre-processed ground penetrating radar profile data after the data denoising processing, wherein the data denoising processing includes direct wave removal processing.

[0069] In a second aspect, a ground penetrating radar-based automatic steel bar positioning device is provided, comprising:

[0070] a data preprocessing unit, configured to obtain GPR profile data of the concrete to be tested, and perform data preprocessing on the GPR profile data to obtain preprocessed GPR profile data, wherein the data preprocessing includes data correction processing and data denoising processing;

[0071] a data processing unit configured to perform reinforcement reflection signal enhancement processing on the pre-processed ground penetrating radar profile data to obtain enhanced profile data, and perform data offset processing on the enhanced profile data to obtain offset profile data;

[0072] a steel bar identification unit, configured to perform steel bar identification processing on the offset profile data to obtain at least one steel bar initial region in the ground penetrating radar profile data;

[0073] A steel bar identification unit is used to perform shape feature extraction processing on each steel bar initial area in the at least one steel bar initial area to obtain steel bar shape features of each steel bar initial area;

[0074] The steel bar identification unit is further configured to determine at least one steel bar reference region in the ground penetrating radar profile data based on the shape characteristics of each steel bar;

[0075] A positioning unit, configured to determine the size information of each steel bar in the concrete to be tested using at least one steel bar reference area;

[0076] The positioning unit is also used to obtain the protective layer thickness and horizontal spacing of the steel bars in the concrete to be tested based on the size information of each steel bar, so as to complete the automatic positioning processing of the steel bars in the concrete to be tested after obtaining the protective layer thickness and horizontal spacing of the steel bars.

[0077] In the third aspect, another automatic steel bar positioning device based on ground penetrating radar is provided. Taking the device as an electronic device as an example, it includes a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the automatic steel bar positioning method based on ground penetrating radar as described in the first aspect or any possible design of the first aspect.

[0078] In a fourth aspect, a storage medium is provided, on which instructions are stored. When the instructions are run on a computer, the method for automatic steel bar positioning based on ground penetrating radar as described in the first aspect or any possible design of the first aspect is executed.

[0079] In a fifth aspect, a computer program product comprising instructions is provided, which, when executed on a computer, causes the computer to execute the method for automatic steel bar positioning based on ground penetrating radar as described in the first aspect or any possible design of the first aspect.

[0080] Beneficial effects:

[0081] (1) The automatic steel bar positioning method provided by the present invention does not require human intervention, can significantly improve data processing efficiency, and can greatly reduce engineering time and cost; at the same time, before positioning, the data is corrected and denoised, so that the interference of noise can be reduced, thereby ensuring the accuracy of the results; further, during positioning, signal enhancement and offset processing are introduced, based on which the profile data of the steel bar signal can be highlighted, thereby facilitating subsequent steel bar identification and analysis; finally, when performing size detection, the shape characteristics of the steel bar are comprehensively analyzed to determine and retain the reference area of ​​the real steel bar, and based on the reference area of ​​the real steel bar, size detection, protective layer thickness detection and horizontal spacing measurement are performed; thereby, the interference area can be further filtered out, thereby achieving accurate measurement of steel bar parameters; based on the above explanation, the present invention provides an innovative solution for ground penetrating radar data processing, and introduces a fully automated, high-precision steel bar positioning and parameter calibration technology, which can greatly improve the efficiency and accuracy of steel bar detection in concrete structures; therefore, it is very suitable for large-scale application and promotion.

[0082] (2) Enhanced consistency and accuracy of detection: Manual judgment is limited by individual experience and subjective judgment, which is prone to errors and omissions; the present invention adopts a unified judgment standard to ensure the consistency and accuracy of the results. In this way, errors caused by human factors can be greatly reduced, and the probability of major detection accidents is reduced.

[0083] (3) Optimized data compensation mechanism: The present invention introduces an advanced data compensation strategy, which effectively solves the problem of weak data signals caused by improper operation or too deep buried steel bars. This strategy ensures the complete capture of steel bar signals, reduces information omissions, and improves data utilization and comprehensiveness of detection.

[0084] (4) Improved accuracy of position positioning and parameter calculation: Compared with the existing technology, the present invention achieves higher accuracy in steel bar positioning and parameter calculation. That is, by comprehensively analyzing the size and signal characteristics of the steel bars, a more accurate calculation method of the protective layer thickness and steel bar spacing is provided, thereby optimizing the accuracy of the structural safety assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 A schematic flow chart of the steps of a method for automatic steel bar positioning based on ground penetrating radar provided in an embodiment of the present invention;

[0086] Figure 2 A schematic diagram showing the comparison of ground penetrating radar profile data before and after reinforcement reflection signal enhancement processing provided by an embodiment of the present invention;

[0087] Figure 3 A schematic diagram of the effect of the profile data after migration provided by an embodiment of the present invention;

[0088] Figure 4 A schematic diagram of the effect of the steel bar reference area provided by an embodiment of the present invention;

[0089] Figure 5 A schematic diagram of the center of a steel bar in a real steel bar area provided by an embodiment of the present invention;

[0090] Figure 6 A schematic diagram illustrating the thickness of the protective layer and the horizontal spacing of each steel bar provided in an embodiment of the present invention;

[0091] Figure 7 A schematic structural diagram of a ground-penetrating radar-based automatic steel bar positioning device provided in an embodiment of the present invention;

[0092] Figure 8 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0093] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be briefly introduced below in conjunction with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0094] It should be understood that although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element can be referred to as a second element, and similarly, a second element can be referred to as a first element without departing from the scope of the exemplary embodiments of the present invention.

[0095] It should be understood that the term "and / or" that may appear in this document is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B may indicate three situations: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" that may appear in this document describes another type of association object relationship, indicating that two relationships may exist. For example, A / and B may indicate two situations: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the previous and subsequent associated objects are in an "or" relationship.

[0096] Example:

[0097] See also Figure 1As shown, the automatic steel bar positioning method based on ground penetrating radar provided in this embodiment provides an innovative solution for ground penetrating radar data processing, and introduces a fully automated, high-precision steel bar positioning and parameter calibration technology, which can overcome the technical difficulties such as the suppression of noise interference in automated processing, the accurate identification and analysis of steel bar signals, and the precise measurement of the thickness of the steel bar protective layer. In this way, the efficiency and accuracy of steel bar detection in concrete structures can be greatly improved; therefore, this method is very suitable for large-scale application and promotion; among them, for example, this method can be but not limited to running on the steel bar positioning end side, optionally, the steel bar positioning end can be but not limited to a personal computer (PC). It can be understood that the aforementioned execution subject does not constitute a limitation on the embodiment of the present application. Accordingly, the operation steps of this method can be but not limited to the following steps S1 to S7.

[0098] S1. Acquire GPR profile data of the concrete to be tested, and perform data preprocessing on the GPR profile data to obtain preprocessed GPR profile data, wherein the data preprocessing includes data correction processing and data denoising processing. In this embodiment, the GPR profile data is essentially a cross-sectional image of the concrete to be tested, and this embodiment uses this as a basis to achieve automatic positioning of internal steel bars. At the same time, the data correction processing is mainly zero-point correction, and the data denoising processing is direct wave removal. Optionally, the data preprocessing process can be, but is not limited to, as shown in the following steps S11 and S12.

[0099] S11. Using a zero-point correction algorithm, the ground-penetrating radar profile data is corrected to obtain corrected ground-penetrating radar profile data. In specific applications, the process of correcting the aforementioned ground-penetrating radar profile data using a zero-point correction algorithm may be, but is not limited to: (1) for the radar data of each channel in the ground-penetrating radar profile data, finding the position of the first maximum point of the radar data in each channel (i.e., the time when the first maximum point is located); (2) calculating the average value of the first maximum position of the radar data in all channels; (3) adjusting the radar data in each channel according to the aforementioned average value, so as to obtain corrected ground-penetrating radar profile data after adjustment.

[0100] In this embodiment, the following formula (7) may be used, for example but not limited to, to adjust the signals in each channel.

[0101]

[0102] In the above formula (7), t max represents the average value of the first maximum position of the signal in all channels, t represents the sampling time, R xRepresents the radar data in the xth channel of the ground penetrating radar profile data, R x ′(t) represents the radar data in x channels after correction.

[0103] In this way, based on the aforementioned step S11, the first significant reflection point of each radar signal (usually the maximum point) can be identified, and this point can be used as the benchmark for the start of each signal; and this benchmark point reflects the propagation time of the radar wave from the antenna to the ground or near the ground interface; based on this, by aligning all radar channels to this benchmark, the underground structure can be analyzed and interpreted more clearly, which is helpful for the subsequent accurate determination of the thickness of the protective layer.

[0104] After completing the zero point correction of the GPR profile data, denoising processing may be performed to remove the interference of the noise signal; wherein the denoising processing process may be, but is not limited to, as shown in the following step S12.

[0105] S12. Perform data denoising on the corrected GPR profile data to obtain the pre-processed GPR profile data after the data denoising, wherein the data denoising includes direct wave removal. In a specific application, the direct wave removal process may be, but is not limited to: (1) for each channel of the corrected GPR profile data, use a length of T d (1) The radar data corresponding to each channel after filtering are obtained by using a moving average filter; (2) The radar data corresponding to each channel after filtering are subtracted from the radar data before filtering in the corresponding channel to obtain the radar data corresponding to each channel after denoising; (3) The radar data corresponding to each channel after denoising are used to form the pre-processed ground penetrating radar profile data.

[0106] In specific applications, the filtering process of the moving average filter can be expressed as:

[0107]

[0108] In the above formula (8), R x ″(t) represents the radar data corresponding to the x-th channel after denoising, and k represents the moving step size of the filter.

[0109] In this way, through the aforementioned step S12, the direct wave in the signal can be filtered out, thereby retaining all reflection features except the direct wave; based on this, the high-amplitude data of air coupling can be eliminated, thereby highlighting the signal of the steel bar reflection echo.

[0110] Based on the aforementioned steps S11 and S12, data preprocessing can be completed to achieve detection consistency and overcome noise interference; then, the steel bar reflection signal can be enhanced and offset processed to further highlight the steel bar reflection signal, thereby facilitating subsequent steel bar identification and positioning; wherein, the signal enhancement and offset processing process can be, but is not limited to, as shown in the following step S2.

[0111] S2. The pre-processed ground penetrating radar profile data is subjected to steel bar reflection signal enhancement processing to obtain enhanced profile data, and the enhanced profile data is subjected to data offset processing to obtain offset profile data; in this embodiment, the reflection echo hyperbola (i.e., profile) generated by the steel bar is subjected to reflection signal enhancement processing, which can suppress the background and smaller structural reflections, and their values ​​tend to 0, while the reflection of the steel bar is enhanced; in this way, the weak signal reflection caused by deep burial depth or improper operation during collection can be enhanced, thereby ensuring the accuracy and comprehensiveness of the data.

[0112] Optionally, for example but not limited to, the following steps S21 to S25 may be used to complete the enhancement processing of the steel bar reflection signal.

[0113] S21. Perform average filtering on the preprocessed GPR profile data to obtain filtered GPR profile data. In this embodiment, average filtering on the preprocessed GPR profile data is performed by calculating the average signal of the entire profile, that is, calculating the mean of the signals in all channels in the preprocessed GPR profile data to obtain the filtered GPR profile data. In this way, the aforementioned average filtering can help determine the reflection features that are prevalent in all channels. After the average filtering is completed, profile differential energy enhancement processing can be performed, and the process can be, but is not limited to, as shown in the following step S22.

[0114] S22. Perform differential enhancement processing on the filtered ground penetrating radar profile data to obtain differential enhanced ground penetrating radar profile data; in specific applications, for example, but not limited to, applying a first-order difference operator to perform differential enhancement processing to highlight significant change features in the profile; wherein, for example, the differential enhancement processing can be, but not limited to, expressed as: D(t) = M(t+1) - M(t), where D(t) represents the differential enhanced ground penetrating radar profile data, M(t) represents the filtered ground penetrating radar profile data, and t represents the sampling time.

[0115] In this way, through the aforementioned step S22, differential processing can be used to highlight rapid changes in the profile, and these changes are usually related to structures such as steel bars; after completing the differential enhancement processing of the data, nonlinear conversion processing of the signal can be performed to enhance the effective signal part; wherein, the nonlinear conversion process of the signal can be but is not limited to as shown in the following step S23.

[0116] S23. Perform nonlinear transformation processing on the differentially enhanced ground-penetrating radar profile data to obtain transformed ground-penetrating radar profile data. In a specific implementation, for example, but not limited to, the following formula (1) can be used to perform nonlinear transformation processing on the differentially enhanced ground-penetrating radar profile data to obtain transformed ground-penetrating radar profile data.

[0117] E(t)=α·log(1+β·|D(t)|) (1)

[0118] In the above formula (1), D(t) represents the differentially enhanced ground penetrating radar profile data, E(t) represents the converted ground penetrating radar profile data, and α and β both represent nonlinear adjustment factors. In this embodiment, α and β can be specifically set according to actual use and are not specifically limited here.

[0119] After the nonlinear transformation of the differential enhanced ground penetrating radar profile data is completed, invalid signal removal processing may be performed, and the process may be, but is not limited to, as shown in the following step S24.

[0120] S24. Perform invalid signal removal processing on the converted ground penetrating radar profile data to obtain valid ground penetrating radar profile data after the invalid signal removal processing; in specific applications, for example, but not limited to, first calculating the average value and standard deviation of the converted ground penetrating radar profile data; then calculating the signal threshold based on the average value and the standard deviation; finally, performing invalid signal removal processing on the converted ground penetrating radar profile data based on the signal threshold to obtain the valid ground penetrating radar profile data after the invalid signal removal processing.

[0121] Optionally, the signal threshold may be calculated by, for example but not limited to, the following formula (2).

[0122] θ=μ+k′·σ (2)

[0123] In the above formula (2), θ represents the signal threshold, μ represents the average value, σ represents the standard deviation, and k′ represents an adjustment parameter, wherein the adjustment parameter can be pre-set and can be specifically set according to actual use.

[0124] In this way, after the signal threshold is obtained based on the above formula (2), invalid signals can be removed according to the signal threshold. That is, if the signal value at the t-th moment in the converted ground penetrating radar profile data is greater than or equal to the above signal threshold, the signal value at the t-th moment is retained; otherwise, the signal value at the t-th moment is changed to 0; based on this, the invalid signals in the converted ground penetrating radar profile data can be removed; finally, the signal is reconstructed to obtain enhanced profile data, and the process can be, but is not limited to, as shown in the following step S25.

[0125] S25. Perform data reconstruction processing on the effective GPR profile data to obtain enhanced profile data after the data reconstruction processing; in this embodiment, it is equivalent to using the effective GPR profile data to reconstruct the entire profile, thereby highlighting important structural features.

[0126] Thus, through the aforementioned steps S21 to S25, the reinforcement reflection signal enhancement processing can be completed, and the background and smaller structural reflections can be suppressed at the same time, thereby reducing the interference of invalid signals on subsequent reinforcement identification and positioning; wherein, the comparison diagram of the ground penetrating radar profile data before and after the reinforcement reflection signal enhancement processing can be seen in Figure 2 As shown, Figure 2 Figure (a) shows the schematic diagram of the steel bar reflection signal before the enhancement process, while Figure (2) (b) shows the schematic diagram after the process. Figure 2 It can be seen that interference signals such as background are suppressed.

[0127] In this embodiment, after completing the enhancement processing of the steel bar reflection signal, data offset processing can be performed, that is, the hyperbolic signal is converged, so as to preliminarily locate the position of the target reflector, that is, the steel bar, to further highlight the steel bar reflection signal; among them, for example, but not limited to, reverse time migration, FK migration, Kirchhoff migration and other methods can be used to perform data offset processing of enhanced profile data.

[0128] Optionally, this embodiment preferably uses the FK migration algorithm to perform migration processing on the enhanced profile data. FK migration can effectively balance accuracy and computational efficiency, thereby efficiently and quickly calculating the results. The main steps of FK migration are:

[0129] Step 1: Data conversion, that is, converting the enhanced profile data in the time domain into the frequency-wavenumber domain. In this embodiment, this can be accomplished by, but is not limited to, a two-dimensional Fourier transform, and the formula is:

[0130]

[0131] In the above formula (9), S(k x ,ω) represents the enhanced profile data in the frequency-wavenumber domain, s(x′,t) represents the enhanced profile data in the time domain, k x represents the wave number, i represents the complex number, x′ represents the spatial coordinate, and ω represents the angular frequency.

[0132] After the data conversion is complete, the offset can be applied as shown in step 2 below.

[0133] Step 2: Apply migration processing to the enhanced profile data in the frequency-wavenumber domain to obtain migration data; for example, but not limited to, the following formula (10) can be used to obtain the migration data.

[0134]

[0135] In the above formula (10), S′(k x ,ω) represents the offset data, k z represents the vertical wave number, z represents the depth, and the vertical wave number can be calculated using the following formula (11).

[0136]

[0137] In the above formula (10), v represents the propagation velocity of the concrete to be tested.

[0138] After applying the offset, the inverse transform can be performed, as shown in step 3 below.

[0139] Step 3: Perform an inverse two-dimensional Fourier transform on the migration data to obtain the migrated profile data after the inverse two-dimensional Fourier transform.

[0140] In this way, through the above steps 1 to 3, the data migration can be completed. After the migration, the cross section has obvious aggregation characteristics, which are displayed as white highlighted areas on the map, representing the approximate range of the steel bars. Therefore, the underground structure location can be displayed more clearly and accurately. The schematic diagram can be, but is not limited to, see Figure 3 shown.

[0141] After completing the data offset, the steel bar identification process can be performed; wherein, this embodiment first performs a preliminary positioning of the area where the steel bars are located, and then, based on the steel bar morphological characteristics, performs a secondary positioning to obtain the reference area of ​​the steel bars; and then, based on the reference area of ​​the steel bars, performs positioning detection; wherein, the aforementioned process can be, but is not limited to, as shown in the following steps S3 to S7.

[0142] S3. Performing steel bar identification processing on the offset profile data to obtain at least one initial steel bar region in the ground penetrating radar profile data; in specific implementation, this embodiment defines a local window and then calculates statistical features within the window to enhance the irregular elliptical local area in the image, thereby completing the preliminary identification of steel bars; wherein the aforementioned process can be but is not limited to the following steps S31 to S34.

[0143] S31. For any pixel point in the offset profile data, obtain the neighborhood area of ​​the any pixel point; in a specific application, for example, the neighborhood area is usually p×p, p is an odd number, which can be but not limited to 3 or 5, so it is equivalent to obtaining 3 neighborhoods or 5 neighborhoods of the any pixel point to perform subsequent local contrast enhancement processing, so as to determine whether the any pixel point is a steel bar pixel point based on the brightness value of the pixel point after local contrast enhancement; wherein, the local contrast enhancement processing is mainly implemented based on the average brightness and brightness standard deviation of the neighborhood area, and the process can be but not limited to the following steps S32 and S33.

[0144] S32. Calculate the average brightness and brightness standard deviation of all neighborhood pixels in the neighborhood area.

[0145] After the average brightness and brightness standard deviation of the neighborhood area are calculated based on the aforementioned step S32, local contrast enhancement processing can be performed on any pixel point. The process can be, but is not limited to, as shown in the following step S33.

[0146] S33. Perform local contrast enhancement processing on any one of the pixel points according to the average brightness and the brightness standard deviation, so as to obtain the locally contrast enhanced any one of the pixel points after the local contrast enhancement processing, and obtain the locally contrast enhanced profile data after polling all the pixel points in the offset profile data; in specific implementation, for example, but not limited to, the following formula (3) can be used to perform local contrast enhancement processing on any one of the pixel points.

[0147] E(x,y)=I(x,y)+γσlocal(x,y)·[I(x,y)-μlocal(x,y)] (3)

[0148] In the above formula (3), I(x, y) represents the brightness of any pixel, σlocal(x, y) represents the brightness standard deviation, μlocal(x, y) represents the average brightness, γ represents the contrast adjustment factor, and E(x, y) represents the brightness of any pixel after local contrast enhancement. In this embodiment, the contrast adjustment factor can be specifically set according to actual use and is not specifically limited here.

[0149] In this way, based on the aforementioned formula (3), the local contrast enhancement processing of any of the aforementioned pixel points can be completed; then, using the same principle, the local contrast enhancement of the remaining pixel points can be completed; finally, the preliminary identification of the steel bars can be performed based on the profile data after local contrast enhancement, and the process can be but is not limited to the following step S34.

[0150] S34. Performing steel bar identification processing on the locally contrast enhanced profile data to obtain at least one steel bar initial region in the ground penetrating radar profile data after the steel bar identification processing; in specific applications, for example, but not limited to, first screening out steel bar pixel points from the locally contrast enhanced profile data based on the brightness value of each pixel point in the locally contrast enhanced profile data and the local average brightness of each pixel point; wherein, for example, the local average brightness of each pixel point is the average brightness of the neighborhood area of ​​each pixel point before local contrast enhancement; and for example, using the brightness value of each pixel point in the locally contrast enhanced profile data, subtracting its local average brightness, and taking the absolute value to obtain the brightness difference; then, taking the pixel point whose brightness difference is greater than the brightness threshold as the steel bar pixel point; in this way, after obtaining the steel bar pixel point, the screened steel bar pixel point can be used to determine at least one steel bar initial region in the ground penetrating radar profile data; in this embodiment, for example, the connected domain formed by the steel bar pixel points is taken as the steel bar initial region.

[0151] In this way, based on the aforementioned steps S31 to S34, the preliminary identification of steel bars can be completed, and at least one initial area of ​​steel bars in the ground penetrating radar profile data can be obtained; then, fine identification of steel bars can be performed to further filter out interference areas; wherein, the fine identification process of steel bars can be but is not limited to the following steps S4 and S5.

[0152] S4. Perform shape feature extraction processing on each of the at least one steel bar initial area to obtain the steel bar shape features of each steel bar initial area; in specific implementation, the corresponding steel bar shape features are extracted mainly based on the area and perimeter of each steel bar initial area; wherein, any steel bar initial area is taken as an example for explanation below, and the process can be but is not limited to the following steps S41 to S43.

[0153] S41. For any steel bar initial area, calculate the area and perimeter of the said any steel bar initial area; in specific applications, for example, the number of pixel points in the any steel bar initial area can be used as the area of ​​the any steel bar initial area; and the number of boundary pixel points of the any steel bar initial area can be used as the perimeter of the any steel bar initial area; in this way, after obtaining the area and perimeter of the aforementioned any steel bar initial area, the geometric and topological characteristics of its corresponding shape can be calculated based on this; wherein, its calculation process can be but is not limited to as shown in the following step S42.

[0154] S42. Calculate the shape complexity of the initial region of any steel bar based on the area and perimeter of the initial region of any steel bar and according to the following formula (4).

[0155]

[0156] In the above formula (4), F represents the shape complexity of the initial region of any steel bar, P represents the perimeter of the initial region of any steel bar, and A represents the area of ​​the initial region of any steel bar.

[0157] Thus, based on the aforementioned formula (4), the shape complexity of the initial area of ​​any steel bar can be calculated, which reflects the degree of closeness of the shape to a perfect circle. The larger the value, the more complex or irregular the shape. Therefore, the shape complexity can be used as the shape feature of the initial area of ​​any steel bar, so that the steel bar can be finely identified based on the shape feature in the future. The process can be, but is not limited to, as shown in the following step S43.

[0158] S43. Using the shape complexity of any of the steel bar initial regions as the steel bar shape feature of any of the steel bar initial regions.

[0159] Thus, through the aforementioned steps S41 to S43, the extraction of the steel bar shape features can be completed; then, the steel bars can be finely identified based on the steel bar shape features, that is, the areas that do not belong to the steel bars can be screened out; wherein, the fine identification process can be but is not limited to the following step S5.

[0160] S5. Based on the shape characteristics of each steel bar, determine at least one steel bar reference area in the ground penetrating radar profile data; in specific implementation, for example, but not limited to, screen out steel bar initial areas with shape complexity less than or equal to a complexity threshold from at least one steel bar initial area as steel bar reference areas; wherein, in this embodiment, for example, the complexity threshold can be, but not limited to, set to 2, that is, if the shape complexity of an area is less than or equal to this threshold, it means that the shape of the area is regular enough and should be retained; conversely, if it is greater than the threshold, it indicates that the shape is too complex or irregular and should be discarded.

[0161] Thus, through the above design, the data can be further filtered to remove the area that does not belong to the steel bar, thereby improving the accuracy of subsequent steel bar positioning; wherein, the effect diagram of the identified steel bar reference area can be but is not limited to referring to Figure 4 shown.

[0162] After obtaining the actual area of ​​each steel bar in the concrete to be tested, size detection can be performed based on this. The principle is: fit a reference area within this range, and the reference area is fitted according to the preliminary area so that the core area of ​​the preliminary area is covered. The center position of the reference area is found and used as the center position of the steel bar. Then, size detection can be performed based on the center position; wherein the above process can be but is not limited to the following step S6.

[0163] S6. Determine the size information of each steel bar in the concrete to be tested using at least one steel bar reference area. In a specific application, this embodiment embeds a circle inside each steel bar reference area so as to fit the center position of the circular steel bar based on this, and reconstruct the size according to the specific size. Taking any steel bar reference area as an example, the size reconstruction process of the corresponding steel bar is explained, which can be but not limited to the following steps S61 to S64.

[0164] S61. For any one of the at least one steel bar reference areas, obtain a set of boundary pixel points of the any one steel bar reference area; in this embodiment, it is equivalent to obtaining pixel points on the boundary of the any one steel bar reference area to form a set of boundary pixel points; then, based on this, a size reconstruction function can be constructed, and the process can be but is not limited to as shown in the following step S62.

[0165] S62. Using the boundary pixel point set and according to the following formula (5), construct a size reconstruction function of the steel bars corresponding to any steel bar reference area.

[0166]

[0167] In the above formula (5), E(x c ,y c , r) represents the size reconstruction function, x c ,y c represents the horizontal and vertical coordinates of the center of the steel bar corresponding to any steel bar reference area in the ground penetrating radar section, r represents the radius of the steel bar corresponding to any steel bar reference area, x represents the horizontal and vertical coordinates of the center of the steel bar corresponding to any steel bar reference area in the ground penetrating radar section, r represents the radius of the steel bar corresponding to any steel bar reference area, m ,y m represents the horizontal coordinate and vertical coordinate of the mth boundary pixel point in the boundary pixel point set, and n represents the total number of boundary pixel points.

[0168] It can be seen from the above formula (5) that the size reconstruction is actually an error function, which can be defined as the square of the difference between the distance from all convenient pixels to the center of the circle and the radius; in this way, the error function can be minimized through an optimization algorithm to obtain the horizontal and vertical coordinates and radius of the center of the circle corresponding to the minimum error function; wherein, the optimization process of the above error function can be but is not limited to the following step S63.

[0169] S63. Minimize the size reconstruction function to obtain the fitting center coordinates and fitting radius of the steel bars corresponding to any steel bar reference area after minimizing the size reconstruction function; in specific applications, for example, but not limited to, gradient descent or genetic algorithm can be used to minimize the aforementioned size reconstruction function (i.e., error function), thereby obtaining the center coordinates and radius with the minimum error; then, based on this, the center coordinates and actual radius of the steel bars corresponding to any steel bar reference area can be obtained, wherein the aforementioned process can be but not limited to as shown in the following step S64.

[0170] S64. According to the fitting circle center coordinates and the fitting radius, the actual circle center coordinates and the actual radius of the steel bar corresponding to the any steel bar reference area are obtained, so as to utilize the actual circle center coordinates and the actual radius of the steel bar corresponding to the any steel bar reference area to form the size information of the steel bar corresponding to the any steel bar reference area; in a specific application process, the fitting circle center coordinates are actually the circle center coordinates of the cross section of the steel bar corresponding to the any steel bar reference area; in this way, the actual circle center coordinates and the actual radius of the steel bar corresponding to the any steel bar reference area can be obtained based on this; wherein, the circle center schematic diagram obtained by fitting the steel bar in the real steel bar area can be, but is not limited to, referring to Figure 5 shown.

[0171] Furthermore, in this embodiment, the center coordinates of the fitted circle are used as the actual center coordinates, and the actual radius information is known; therefore, when the actual radius information is unknown, if it is from the same batch, its average value is used as the actual radius; if it is from different batches, it is subdivided into different batches according to the size of the fitted circle, and its radius is calculated separately.

[0172] Therefore, through the aforementioned steps S61 to S64, the size information of the steel bars in each steel bar reference area can be obtained; then, based on the size information, the concrete cover thickness of each steel bar and the horizontal spacing between adjacent steel bars can be determined; wherein, the calculation process of the cover thickness and the horizontal spacing can be but is not limited to as shown in the following step S7.

[0173] S7. Based on the size information of each steel bar, the protective layer thickness and the horizontal spacing of each steel bar in the concrete to be tested are obtained, so as to complete the automatic positioning processing of the steel bars in the concrete to be tested after the protective layer thickness and the horizontal spacing of the steel bars are obtained; in this embodiment, any steel bar is taken as an example for specific explanation, and the calculation process of its protective layer thickness and the horizontal spacing between adjacent steel bars can be but is not limited to the following steps S71 to S74.

[0174] S71. Determine the vertex coordinates of any one of the steel bars based on the actual center coordinates and actual radius. In this embodiment, it is equivalent to taking the actual center coordinates of any one of the steel bars as the starting point, drawing a perpendicular line vertically upward, and the distance of the perpendicular line is the actual radius. Then, the intersection of the perpendicular line and the boundary circle of any one of the steel bars is used as the vertex (that is, the upper vertex). In this way, it is equivalent to the horizontal coordinate of the vertex being the same as the horizontal coordinate of the center of the circle of any one of the steel bars, and the vertical coordinate is the vertical coordinate of the center of the circle plus the actual radius.

[0175] After obtaining the vertex coordinates of any steel bar, the thickness of the concrete cover of any steel bar can be calculated based on the vertex coordinates. The calculation process can be, but is not limited to, as shown in the following step S72.

[0176] S72. Calculate the protective layer thickness of any of the steel bars based on the vertex coordinates of the any of the steel bars and in accordance with the following formula (6).

[0177]

[0178] In the above formula (6), h represents the thickness of the protective layer of any steel bar, y d represents the vertical coordinate of the vertex coordinate of any steel bar, T represents the total sampling time of the ground penetrating radar profile data, N represents the number of sampling points of the ground penetrating radar profile data, ε r Indicates the dielectric constant of the concrete to be tested.

[0179] In this way, based on the above formula (6), after calculating the protective layer thickness of any steel bar, the horizontal spacing of the steel bars can be calculated, and the process can be but not limited to the following steps S73 and S74.

[0180] S73. Obtain the vertex coordinates of the steel bars adjacent to the any one steel bar in the concrete to be tested; in this embodiment, the vertex coordinates of the steel bars adjacent to the any one steel bar on both sides are obtained, so as to calculate the horizontal spacing between the any one steel bar and the adjacent steel bars on both sides based on the vertex coordinates of the steel bars adjacent to the any one steel bar on both sides; wherein the calculation process can be, but is not limited to, as shown in the following step S74.

[0181] S74. Calculate the horizontal spacing between any one steel bar and the adjacent steel bar according to the horizontal coordinates of the vertex coordinates of any one steel bar and the horizontal coordinates of the vertex coordinates of the steel bar adjacent to any one steel bar; in specific implementation, the horizontal spacing between the adjacent steel bars on the right is taken as an example for explanation. For example, it can be, but not limited to, first obtaining the track spacing between any one steel bar and the adjacent steel bar on the right; then, calculate the difference between the horizontal coordinates of the vertex of the adjacent steel bar on the right of any one steel bar and the horizontal coordinates of the vertex of any one steel bar; finally, use the difference multiplied by the track spacing (which is the distance set during the instrument acquisition process at which a signal is transmitted once, and in the data it means the actual distance between every two columns of data) to obtain the horizontal spacing between any one steel bar and the adjacent steel bar on the right; of course, the calculation process of the horizontal spacing between it and the adjacent steel bar on the left is the same, and the calculation process of the protective layer thickness and the horizontal spacing of the steel bars of the remaining steel bars is the same, which will not be elaborated here.

[0182] Based on the above steps S71 to S74, the calculation of the protective layer thickness and horizontal spacing of each steel bar can be completed. Among them, the steel bar positioning is performed using the method provided in this embodiment. After actual measurement and verification, the position of the steel bar is accurate, and the steel bar can be quickly and accurately identified in engineering, so that the post-processing capability is enhanced, the detection efficiency is improved, and the recognition accuracy is improved; at the same time, the annotation schematic diagram of the protective layer thickness and horizontal spacing of each steel bar can be, but is not limited to, as follows Figure 6 shown, and Figure 6 The error between the test results shown and the actual burial is less than 3%.

[0183] In addition, in this embodiment, the aforementioned annotated schematic diagram may be displayed visually, for example but not limited to, that is, the aforementioned calculated information is displayed in a cross-sectional diagram. In this way, the position of the steel bars and related parameter information are clearly visible through an intuitive graphical interface, which facilitates rapid evaluation and decision-making by engineers and technicians.

[0184] Therefore, the automatic steel bar positioning method based on ground penetrating radar described in detail in the aforementioned steps S1 to S7 provides an innovative solution for ground penetrating radar data processing, and introduces a fully automated, high-precision steel bar positioning and parameter calibration technology, which can overcome technical difficulties such as the suppression of noise interference in automated processing, accurate identification and analysis of steel bar signals, and precise measurement of the thickness of steel bar protective layer. In this way, the efficiency and accuracy of steel bar detection in concrete structures can be greatly improved; therefore, the present invention is very suitable for large-scale application and promotion.

[0185] like Figure 7 As shown, the second aspect of this embodiment provides a hardware device for implementing the automatic steel bar positioning method based on ground penetrating radar described in the first aspect of the embodiment, including:

[0186] The data preprocessing unit is used to obtain the ground penetrating radar profile data of the concrete to be tested, and perform data preprocessing on the ground penetrating radar profile data to obtain preprocessed ground penetrating radar profile data, wherein the data preprocessing includes data correction processing and data denoising processing.

[0187] The data processing unit is used to perform reinforcement reflection signal enhancement processing on the pre-processed ground penetrating radar profile data to obtain enhanced profile data, and perform data offset processing on the enhanced profile data to obtain offset profile data.

[0188] The steel bar identification unit is used to perform steel bar identification processing on the offset profile data to obtain at least one steel bar initial area in the ground penetrating radar profile data.

[0189] The steel bar identification unit is used to perform shape feature extraction processing on each steel bar initial area in the at least one steel bar initial area to obtain the steel bar shape feature of each steel bar initial area.

[0190] The steel bar identification unit is further used to determine at least one steel bar reference area in the ground penetrating radar profile data based on the shape characteristics of each steel bar.

[0191] The positioning unit is used to determine the size information of each steel bar in the concrete to be tested by using at least one steel bar reference area.

[0192] The positioning unit is also used to obtain the protective layer thickness and horizontal spacing of the steel bars in the concrete to be tested based on the size information of each steel bar, so as to complete the automatic positioning processing of the steel bars in the concrete to be tested after obtaining the protective layer thickness and horizontal spacing of the steel bars.

[0193] The working process, working details and technical effects of the device provided in this embodiment can be found in the first aspect of the embodiment and will not be described in detail here.

[0194] like Figure 8 As shown, the third aspect of this embodiment provides another automatic steel bar positioning device based on ground penetrating radar. Taking the device as an electronic device as an example, it includes: a memory, a processor and a transceiver that are communicatively connected in sequence, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the automatic steel bar positioning method based on ground penetrating radar as described in the first aspect of the embodiment.

[0195] For example, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in first-out memory (FIFO), and / or first-in last-out memory (FILO); specifically, the processor may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. The processor may be implemented in at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Furthermore, the processor may include a main processor and a coprocessor. The main processor is a processor for processing data in an awake state, also known as a CPU (Central Processing Unit); and the coprocessor is a low-power processor for processing data in a standby state.

[0196] In some embodiments, the processor may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. For example, the processor may be, but is not limited to, a microprocessor of the STM32F105 series, a reduced instruction set computer (RISC) microprocessor, an X86 architecture processor, or a processor with an integrated embedded neural network processing unit (NPU); the transceiver may be, but is not limited to, a wireless fidelity (WIFI) wireless transceiver, a Bluetooth wireless transceiver, a general packet radio service technology (GPRS) wireless transceiver, a ZigBee protocol (a low-power local area network protocol based on the IEEE802.15.4 standard, ZigBee) wireless transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. In addition, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0197] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first aspect of the embodiment and will not be described in detail here.

[0198] The fourth aspect of this embodiment provides a storage medium that stores instructions for the automatic steel bar positioning method based on ground penetrating radar described in the first aspect of the embodiment, that is, the storage medium stores instructions, and when the instructions are run on a computer, the automatic steel bar positioning method based on ground penetrating radar as described in the first aspect of the embodiment is executed.

[0199] The storage medium refers to a carrier for storing data, which may include but is not limited to a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive and / or a memory stick, and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0200] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment and will not be described in detail here.

[0201] The fifth aspect of this embodiment provides a computer program product containing instructions, which, when executed on a computer, enables the computer to execute the method for automatic steel bar positioning based on ground penetrating radar as described in the first aspect of the embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0202] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A method for automatic steel bar positioning based on ground penetrating radar, characterized in that: include: Acquiring ground-penetrating radar profile data of the concrete to be tested, and performing data preprocessing on the ground-penetrating radar profile data to obtain preprocessed ground-penetrating radar profile data, wherein the data preprocessing includes data correction processing and data denoising processing; Performing reinforcement reflection signal enhancement processing on the pre-processed ground penetrating radar profile data to obtain enhanced profile data, and performing data offset processing on the enhanced profile data to obtain offset profile data; performing steel bar identification processing on the offset profile data to obtain at least one steel bar initial region in the ground penetrating radar profile data; Performing shape feature extraction processing on each steel bar initial region in the at least one steel bar initial region to obtain steel bar shape features of each steel bar initial region; determining at least one steel bar reference region in the ground penetrating radar profile data based on the shape characteristics of each steel bar; Determining the size information of each steel bar in the concrete to be tested using at least one steel bar reference area; According to the size information of each steel bar, the protective layer thickness and the horizontal spacing of each steel bar in the concrete to be tested are obtained, so as to complete the automatic positioning processing of the steel bars in the concrete to be tested after the protective layer thickness and the horizontal spacing of the steel bars are obtained; The step of performing shape feature extraction on each of the at least one steel bar initial region to obtain steel bar shape features of each steel bar initial region includes: For any initial area of ​​a steel bar, calculate the area and perimeter of the initial area of ​​the steel bar; According to the area and perimeter of the initial region of any steel bar, and in accordance with the following formula (4), the shape complexity of the initial region of any steel bar is calculated; In the above formula (4), F represents the shape complexity of the initial region of any steel bar, P represents the perimeter of the initial region of any steel bar, and A represents the area of ​​the initial region of any steel bar; Using the shape complexity of any one of the steel bar initial regions as the steel bar shape feature of the any one of the steel bar initial regions; Accordingly, determining at least one steel bar reference area in the GPR profile data based on the shape characteristics of each steel bar includes: From the at least one steel bar initial region, a steel bar initial region having a shape complexity less than or equal to a complexity threshold is screened out as a steel bar reference region.

2. The method according to claim 1, characterized in that The pre-processed GPR profile data is subjected to reinforcement reflection signal enhancement processing to obtain enhanced profile data, including: Performing average filtering on the pre-processed ground penetrating radar profile data to obtain filtered ground penetrating radar profile data; Performing differential enhancement processing on the filtered ground penetrating radar profile data to obtain differential enhanced ground penetrating radar profile data; Performing nonlinear transformation on the differential enhanced ground penetrating radar profile data to obtain transformed ground penetrating radar profile data; performing invalid signal removal processing on the converted ground penetrating radar profile data, so as to obtain valid ground penetrating radar profile data after the invalid signal removal processing; Data reconstruction processing is performed on the effective ground penetrating radar profile data to obtain enhanced profile data after the data reconstruction processing.

3. The method according to claim 2, characterized in that The differential enhanced ground penetrating radar profile data is subjected to nonlinear transformation processing to obtain the transformed ground penetrating radar profile data, including: The differential enhanced ground penetrating radar profile data is subjected to nonlinear transformation processing using the following formula (1) to obtain transformed ground penetrating radar profile data; E(t ) = α·log(1+ β·|D(t )|) (1) In the above formula (1), D(t) represents the differential enhanced ground penetrating radar profile data, E(t) represents the converted ground penetrating radar profile data, and α and β both represent nonlinear adjustment factors; Accordingly, performing invalid signal removal processing on the converted ground penetrating radar profile data to obtain valid ground penetrating radar profile data after the invalid signal removal processing includes: Calculating the mean and standard deviation of the converted ground penetrating radar profile data; According to the mean value and the standard deviation, the signal threshold is calculated using the following formula (2); θ = μ + k′·σ (2) In the above formula (2), θ represents the signal threshold, μ represents the average value, σ represents the standard deviation, and k′ represents the adjustment parameter; According to the signal threshold, invalid signal removal processing is performed on the converted ground penetrating radar profile data to obtain the valid ground penetrating radar profile data after the invalid signal removal processing.

4. The method according to claim 1, wherein Performing steel bar identification processing on the offset profile data to obtain at least one steel bar initial region in the ground penetrating radar profile data includes: For any pixel point in the offset profile data, obtaining a neighborhood area of ​​the any pixel point; Calculate the average brightness and brightness standard deviation of all neighboring pixels in the neighborhood area; performing a local contrast enhancement process on the any pixel point according to the average brightness and the brightness standard deviation, so as to obtain the any pixel point after the local contrast enhancement process, and obtaining the locally contrast enhanced profile data after polling all pixel points in the offset profile data; The section data after local contrast enhancement is subjected to steel bar identification processing, so as to obtain at least one steel bar initial region in the ground penetrating radar section data after the steel bar identification processing.

5. The method according to claim 4, characterized in that Performing local contrast enhancement processing on any pixel point according to the average brightness and the brightness standard deviation, including: The following formula (3) is used to perform local contrast enhancement processing on any pixel point; E(x, y) = I(x, y)+ γσlocal(x, y)·[I (x, y)- μlocal(x, y)] (3) In the above formula (3), I(x,y) represents the brightness of any pixel, σlocal(x,y) represents the brightness standard deviation, μlocal(x,y) represents the average brightness, γ represents the contrast adjustment factor, and E(x,y) represents the brightness of any pixel after local contrast enhancement; Accordingly, performing steel bar identification processing on the locally contrast-enhanced profile data to obtain at least one steel bar initial region in the ground penetrating radar profile data after the steel bar identification processing includes: Screening out steel bar pixels from the locally contrast enhanced cross-sectional data according to a brightness value of each pixel in the locally contrast enhanced cross-sectional data and a local average brightness of each pixel, wherein the local average brightness of each pixel is an average brightness of a neighborhood area of ​​each pixel before the local contrast enhancement is performed; At least one initial steel bar region in the ground penetrating radar profile data is determined using the screened steel bar pixel points.

6. The method according to claim 1, wherein Using at least one steel bar reference region, determining the size information of each steel bar in the concrete to be tested includes: For any one of the at least one steel bar reference area, obtaining a set of boundary pixel points of the any one steel bar reference area; Using the boundary pixel point set and according to the following formula (5), a size reconstruction function of the steel bar corresponding to any steel bar reference area is constructed; In the above formula (5), E(x c ,y c , r) represents the size reconstruction function, x c ,y c represents the horizontal and vertical coordinates of the center of the steel bar corresponding to any steel bar reference area in the ground penetrating radar section, r represents the radius of the steel bar corresponding to any steel bar reference area, x represents the horizontal and vertical coordinates of the center of the steel bar corresponding to any steel bar reference area in the ground penetrating radar section, r represents the radius of the steel bar corresponding to any steel bar reference area, m ,y m represents the horizontal coordinate and vertical coordinate of the mth boundary pixel point in the boundary pixel point set, and n represents the total number of boundary pixel points; Minimizing the size reconstruction function to obtain the fitting center coordinates and fitting radius of the steel bar corresponding to any steel bar reference area after minimizing the size reconstruction function; According to the fitting center coordinates and the fitting radius, the actual center coordinates and actual radius of the steel bars corresponding to any steel bar reference area are obtained, so as to utilize the actual center coordinates and actual radius of the steel bars corresponding to any steel bar reference area to form the size information of the steel bars corresponding to any steel bar reference area.

7. The method according to claim 1, characterized in that The dimensional information of any steel bar includes the actual center coordinates and actual radius of the steel bar. The protective layer thickness and horizontal spacing of each steel bar in the concrete to be tested are obtained based on the dimensional information of each steel bar, including: Determining the vertex coordinates of any one of the steel bars according to the actual center coordinates and actual radius of any one of the steel bars; According to the vertex coordinates of any one of the steel bars and in accordance with the following formula (6), the protective layer thickness of any one of the steel bars is calculated; In the above formula (6), h represents the thickness of the protective layer of any steel bar, y d represents the vertical coordinate of the vertex coordinate of any steel bar, T represents the total sampling time of the ground penetrating radar profile data, N represents the number of sampling points of the ground penetrating radar profile data, ε r Indicates the dielectric constant of the concrete to be tested; Obtaining vertex coordinates of a steel bar adjacent to any one of the steel bars in the concrete to be tested; The horizontal spacing between any one steel bar and the adjacent steel bar is calculated according to the horizontal coordinate of the vertex coordinates of any one steel bar and the horizontal coordinate of the vertex coordinates of the steel bar adjacent to the any one steel bar.

8. The method according to claim 1, characterized in that Performing data preprocessing on the ground penetrating radar profile data to obtain preprocessed ground penetrating radar profile data includes: Using a zero-point correction algorithm, the ground-penetrating radar profile data is corrected to obtain corrected ground-penetrating radar profile data; The corrected ground penetrating radar profile data is subjected to data denoising processing to obtain the pre-processed ground penetrating radar profile data after the data denoising processing, wherein the data denoising processing includes direct wave removal processing.

9. A steel bar automatic positioning device based on ground penetrating radar, applied to the steel bar automatic positioning method based on ground penetrating radar according to any one of claims 1 to 8, characterized in that: include: The data preprocessing unit is used to obtain the GPR profile data of the concrete to be tested and perform data preprocessing on the GPR profile data to obtain preprocessed GPR profile data, wherein the data preprocessing includes data correction processing. Processing and data denoising; a data processing unit configured to perform reinforcement reflection signal enhancement processing on the pre-processed ground penetrating radar profile data to obtain enhanced profile data, and perform data offset processing on the enhanced profile data to obtain offset profile data; a steel bar identification unit, configured to perform steel bar identification processing on the offset profile data to obtain at least one steel bar initial region in the ground penetrating radar profile data; A steel bar identification unit is used to perform shape feature extraction processing on each steel bar initial area in the at least one steel bar initial area to obtain steel bar shape features of each steel bar initial area; The steel bar identification unit is further configured to determine at least one steel bar reference region in the ground penetrating radar profile data based on the shape characteristics of each steel bar; A positioning unit, configured to determine the size information of each steel bar in the concrete to be tested using at least one steel bar reference area; The positioning unit is also used to obtain the protective layer thickness and horizontal spacing of the steel bars in the concrete to be tested based on the size information of each steel bar, so as to complete the automatic positioning processing of the steel bars in the concrete to be tested after obtaining the protective layer thickness and horizontal spacing of the steel bars.

Images