CN113064166B - Method and device for detecting thickness of thin layer defect of multilayer concrete structure and terminal - Google Patents
Method and device for detecting thickness of thin layer defect of multilayer concrete structure and terminal Download PDFInfo
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
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- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
Abstract
The invention is suitable for the technical field of concrete structure detection, and provides a method for detecting the thickness of a defect of a thin layer of a multilayer concrete structure, which comprises the following steps: acquiring an echo signal, wherein the echo signal is a signal returned by a ground penetrating radar for detecting the concrete structure; preprocessing the echo signal by utilizing fractional order S transformation to strengthen signal characteristics corresponding to the thin-layer defect in the echo signal; generating a gray image based on the preprocessed echo signal; inputting the gray level image into the trained thickness recognition model to obtain the thickness information of the thin layer defect in the concrete structure. The method and the device perform characteristic enhancement processing on the echo signals, can improve the detection accuracy of the thickness information of the thin layer defects, acquire the thickness information of the thin layer defects through the thickness identification model, and can determine the thickness information of the thin layer defects caused by a plurality of factors.
Description
Technical Field
The invention belongs to the technical field of concrete structure detection, and particularly relates to a method, a device and a terminal for detecting the thickness of a defect of a thin layer of a multilayer concrete structure.
Background
The multilayer concrete structure is an important structural type of ballastless tracks and tunnel linings of high-speed railways. In a multi-layer concrete structure, different concrete slabs deform due to the temperature, which causes warping and the formation of cracks. The thickness of the gap directly influences the stability and the smoothness of the structure, and the thickness information for measuring the gap defect is always the focus problem of the quality detection of the multilayer concrete structure.
The existing ground penetrating radar method can realize large-scale void defect detection by analyzing different interface echoes, but for a thin-layer gap defect, the size is small, the superposition and aliasing of upper and lower interface echo signals are serious, and the defect thickness is difficult to determine.
Disclosure of Invention
In view of this, the invention provides a method, a device and a terminal for detecting the thickness of a thin layer defect of a multilayer concrete structure, so as to solve the problem that the thickness of a thin layer crack defect is difficult to determine in the prior art.
The first aspect of the embodiment of the invention provides a method for detecting the thickness of a defect of a thin layer of a multilayer concrete structure, which comprises the following steps:
acquiring echo signals, wherein the echo signals are signals returned by a ground penetrating radar for detecting the concrete structure;
preprocessing the echo signal by utilizing fractional order S transformation to strengthen signal characteristics corresponding to the thin-layer defect in the echo signal;
generating a gray image based on the preprocessed echo signals;
and inputting the gray level image into the trained thickness recognition model to obtain the thickness information of the thin layer defect in the concrete structure.
A second aspect of an embodiment of the present invention provides a device for detecting a defect thickness of a thin layer of a multi-layer concrete structure, including:
the signal acquisition module is used for acquiring echo signals, wherein the echo signals are returned by the ground penetrating radar for detecting the concrete structure;
the characteristic strengthening module is used for preprocessing the echo signal by utilizing fractional order S transformation so as to strengthen the signal characteristic corresponding to the thin-layer defect in the echo signal;
the image conversion module is used for generating a gray image based on the preprocessed echo signal;
and the thickness analysis module is used for inputting the gray level image into the trained thickness recognition model to obtain the thickness information of the thin layer defect in the concrete structure.
A third aspect of embodiments of the present invention provides a terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for detecting the defect thickness of the thin layer of the multi-layer concrete structure when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any one of the methods for detecting a defect thickness of a thin layer of a multi-layer concrete structure.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a method for detecting the defect thickness of a thin layer of a multilayer concrete structure, which comprises the following steps: acquiring echo signals, wherein the echo signals are signals returned by a ground penetrating radar for detecting the concrete structure; preprocessing the echo signal by utilizing fractional order S transformation to strengthen signal characteristics corresponding to the thin-layer defect in the echo signal; generating a gray image based on the preprocessed echo signal; and inputting the gray level image into the trained thickness recognition model to obtain the thickness information of the thin layer defect in the concrete structure. The method and the device perform characteristic enhancement processing on the echo signals, can improve the detection accuracy of the thickness information of the thin layer defects, acquire the thickness information of the thin layer defects through the thickness identification model, and can determine the thickness information of the thin layer defects caused by a plurality of factors.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flow chart of an implementation of a method for detecting a defect thickness of a thin layer of a multi-layer concrete structure according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a device for detecting the thickness of a defect in a thin layer of a multi-layer concrete structure according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a terminal provided in an embodiment of the present invention;
FIG. 4 is a graph comparing S-transforms and cross-entropy values corresponding to different fractional factor values in an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a neural network provided in an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, it shows a flowchart of an implementation of the method for detecting a thickness of a defect in a thin layer of a multi-layer concrete structure according to an embodiment of the present invention, which is detailed as follows:
the embodiment of the invention provides a method for detecting the thickness of a defect of a thin layer of a multilayer concrete structure, which comprises the following steps:
101, acquiring an echo signal, wherein the echo signal is a signal returned by a ground penetrating radar for detecting a concrete structure;
in this embodiment, preprocessing the echo signal by using fractional order S transform to enhance the signal characteristics of the echo signal corresponding to the thin layer defect includes:
determining fractional factors in fractional order S transformation;
optionally, determining the fractional factor in the fractional order S transform includes:
calculating the cross entropy of the echo signal when only containing one signal and the echo signal simultaneously contains the upper surface reflection signal and the lower surface reflection signal according to a preset cross entropy formula, wherein the cross entropy formula is as follows:
wherein, the first and the second end of the pipe are connected with each other,
to represent
And
the cross-entropy between the two is,
representing an echo signal containing only one signal,
representing an echo signal containing both an upper surface reflection signal and a lower surface reflection signal;
and taking the fraction factor which enables the cross entropy to appear at most a plurality of minimum values as the determined fraction factor.
In this embodiment, the optimal fractional order is determined to ensure that the rotated S transform contains as much information as possible, and the optimal fractional order may be determined by a minimum cross entropy method. If the thickness of the thin layer defect approaches 0, the echo signal only has one echo signal, namely
y 0 (t)＝αy 1 (t)
When the echo signal contains both upper and lower surface echoes of a thin layer defect, the echo signal is expressed as
y i (t)＝αy 1 (t)+βy 1 (t-Δt i )
y 0 (t) and y i The a-order fraction S of (t) is expressed as
And
defining the alpha-order fraction S transform of the echo signal of the thin-layer defect
And
the cross entropy between is:
the loss function is then:
fig. 4 shows the Ricker wavelet time domain waveform, S transform waveform, different fractional order S transform waveform and cross entropy waveform when the signal-to-noise ratio SNR =20dB after normalization.
From the cross entropy display results of fig. 4, when a =0.44, n =5 and 10 each have the smallest cross entropy with n =0. It is considered herein that the fractional order S transform has the most abundant features when a = 0.44.
The numerical range of the time domain interval of the Ricker wavelet is [0.1,1], the frequency domain interval [0.3fM and 1.8fM ] is taken as a characteristic interval (fM is the central frequency of the Ricker wavelet), and the corresponding a =0.44 order fractional order S is transformed into a characteristic value.
Meanwhile, in order to further eliminate the influence brought by different input wavelets, in the actual processing process, the characteristic value when a =0.44 is defined as:
carrying out fractional order S transformation on the echo signal according to a preset fractional order S transformation formula, wherein the fractional order S transformation formula is as follows:
wherein, frST y (t, f, a) represents an echo signal after fractional order S conversion, Y a (τ) represents a Fourier transform signal of the echo signal, and a represents a fractional factor.
In this embodiment, in order to improve the identification precision of the thin layer defect of the multilayer concrete structure, the ground penetrating radar a-Scan signal needs to be preprocessed.
Defining the A-Scan signal as y (t), its fractional Fourier transform is:
wherein a represents the order of the fractional Fourier transform, and the corresponding rotation angle is
K a (u, t) represents the kernel function of the fractional Fourier transform, and the expression is:
the fractional order of a, frST, defining y (t) is:
in this case, fractional order S transformation can be understood as S transformation at t, f plane rotation angles
This operation will increase the characteristics of the thin layer defect signal in the main frequency region of the ground penetrating radar wavelet.
103, generating a gray image based on the preprocessed echo signal;
in this embodiment, generating a grayscale image based on the preprocessed echo signals includes:
calculating a gray level image corresponding to the preprocessed echo signal through a preset gray level image conversion formula, wherein the gray level image conversion formula is as follows:
wherein, I represents a gray-scale image,
the absolute value of the preprocessed echo signal is shown, and t and f represent the plane where the S transformation is located.
And 104, inputting the gray level image into the trained thickness recognition model to obtain the thickness information of the thin layer defect in the concrete structure.
In this embodiment, before inputting the grayscale image into the trained thickness recognition model, the method further includes:
establishing an echo signal model base, wherein the echo signal model base comprises echo signals corresponding to multiple layers of concrete with different thin-layer defect thicknesses;
in this embodiment, establishing the echo signal model library includes:
acquiring an echo signal for establishing an echo signal model library, wherein the echo signal for establishing the echo signal model library is a signal returned by a ground penetrating radar for detecting a concrete structure with known thickness information of a thin layer defect;
preprocessing an echo signal for establishing an echo signal model library by utilizing fractional order S transformation so as to strengthen signal characteristics corresponding to thin-layer defects in the echo signal for establishing the echo signal model library;
and taking the processed echo signals for establishing an echo signal model library and the thickness information of the corresponding thin layer defects as data in the echo signal model library.
In the present embodiment, the echo signal model library takes valuesA method combining simulation and model test. The numerical model adopts a finite time domain difference method to construct different thin interlayer thickness models. If the concrete internal cavity defect is located on a shallow surface layer, the filling medium is air, the concrete internal cavity defect is divided into (1mm ) grids, the sampling time is 6ns, the calculated value of the number of sampling points is about 1600 sampling points, the cavity defect calculation interval is 7ns x 3 x 1011/1024 approximately 2mm, and the minimum cavity thickness is 1mm when the two-way travel is considered. The model is divided into three layers, the upper and lower layers are concrete, and l is shown in FIG. 5, so that the upper layer echo and the lower layer echo of the model do not generate aliasing with the cavity echo 0 And l 1 30cm and 20cm respectively.
And training the thickness recognition model through an echo signal model library to obtain the trained thickness recognition model.
In this embodiment, in order to identify the thickness information of the thin layer defect in the concrete structure, a convolutional neural network is constructed as a thickness identification model, and fig. 5 is a structure of the neural network. Wherein the input content of the input layer is a gray-scale image converted by the absolute value of fractional-order S-transform data, i.e.
The middle layer is 3 convolution layers, wherein the activation function is a Relu function; the output layer comprises a full connection layer, namely a Softmax logistic regression layer, and the output value of the Softmax logistic regression layer is Oi.
In the actual measurement process, the defect thickness is represented as the number of sampling points in echo data, so that the sampling point interval is adopted as a true value ti in the model and is marked in an One-hot form, and the cross entropy is as follows:
and training the neural network through an echo signal model library to optimize parameters of each layer in the neural network.
According to the method, echo signals are obtained firstly, wherein the echo signals are returned by the ground penetrating radar for detecting the concrete structure; then, preprocessing the echo signal by utilizing fractional order S transformation to strengthen the signal characteristics corresponding to the thin-layer defects in the echo signal; then, generating a gray image based on the preprocessed echo signal; and finally, inputting the gray level image into the trained thickness recognition model to obtain the thickness information of the thin layer defect in the concrete structure. The method and the device perform characteristic enhancement processing on the echo signals, can improve the detection accuracy of the thickness information of the thin layer defects, acquire the thickness information of the thin layer defects through the thickness identification model, and can determine the thickness information of the thin layer defects caused by a plurality of factors.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The following are embodiments of the apparatus of the invention, reference being made to the corresponding method embodiments described above for details which are not described in detail therein.
Fig. 2 is a schematic structural diagram of a device for detecting a thickness of a thin layer defect of a multi-layer concrete structure according to an embodiment of the present invention, which only shows the parts related to the embodiment of the present invention for convenience of description, and the details are as follows:
as shown in fig. 2, the apparatus 2 for detecting a defect thickness of a thin layer of a multi-layer concrete structure comprises:
the signal acquisition module 21 is configured to acquire an echo signal, where the echo signal is a signal returned by a ground penetrating radar to detect a concrete structure;
the characteristic strengthening module 22 is used for preprocessing the echo signal by utilizing fractional order S transformation so as to strengthen the signal characteristics corresponding to the thin layer defects in the echo signal;
the image conversion module 23 is configured to generate a grayscale image based on the preprocessed echo signal;
and the thickness analysis module 24 is used for inputting the gray level image into the trained thickness recognition model to obtain the thickness information of the thin layer defect in the concrete structure.
In this embodiment, the apparatus for detecting a defect thickness of a thin layer of a multi-layer concrete structure further includes:
the model base establishing module is used for establishing an echo signal model base before inputting the gray level image into the trained thickness recognition model, wherein the echo signal model base comprises echo signals corresponding to the multi-layer concrete with different thin-layer defect thicknesses;
and the model training module is used for training the thickness recognition model through the echo signal model library before inputting the gray level image into the trained thickness recognition model to obtain the trained thickness recognition model.
In this embodiment, the model base building module includes:
the system comprises a signal acquisition unit, a signal processing unit and a signal processing unit, wherein the signal acquisition unit is used for acquiring echo signals for establishing an echo signal model library, and the echo signals for establishing the echo signal model library are signals returned by a ground penetrating radar for detecting a concrete structure with known thickness information of a thin layer defect;
the preprocessing unit is used for preprocessing the echo signals for establishing the echo signal model library by utilizing fractional order S transformation so as to strengthen the signal characteristics corresponding to the thin layer defects in the echo signals for establishing the echo signal model library;
and the data modification unit is used for taking the processed echo signals for establishing an echo signal model base and the thickness information of the corresponding thin-layer defects as data in the echo signal model base.
In this embodiment, the image conversion module is further configured to:
calculating a gray level image corresponding to the preprocessed echo signal through a preset gray level image conversion formula, wherein the gray level image conversion formula is as follows:
wherein, I represents a gray-scale image,
representing preprocessed echo signalsThe absolute value of the number, t, f, represents the plane in which the S transform lies.
In this embodiment, the feature enhancing module further includes:
a fractional factor determination unit for determining a fractional factor in fractional order S-transform;
the fractional order S transformation unit is used for performing fractional order S transformation on the echo signal according to a preset fractional order S transformation formula, wherein the fractional order S transformation formula is as follows:
wherein, frST y (t, f, a) represents an echo signal after fractional order S conversion, Y a (τ) represents a Fourier transform signal of the echo signal, and a represents a fractional factor.
In this embodiment, determining the fractional factor in the fractional order S transform comprises:
calculating the cross entropy of the echo signal when only containing one signal and the echo signal simultaneously contains the upper surface reflection signal and the lower surface reflection signal according to a preset cross entropy formula, wherein the cross entropy formula is as follows:
wherein the content of the first and second substances,
to represent
And
the cross-entropy between the two is,
representing an echo signal containing only one signal,
representing an echo signal containing both an upper surface reflection signal and a lower surface reflection signal;
and taking the fraction factor which enables the cross entropy to appear at most as a minimum value as the determined fraction factor.
Fig. 3 is a schematic diagram of a terminal according to an embodiment of the present invention. As shown in fig. 3, the terminal 3 of this embodiment includes: a processor 30, a memory 31 and a computer program 32 stored in said memory 31 and executable on said processor 30. The processor 30 executes the computer program 32 to implement the steps of the above-mentioned each embodiment of the method for detecting the thickness of the defect of the thin layer of the multi-layer concrete structure, such as the steps 101 to 103 shown in fig. 1. Alternatively, the processor 30, when executing the computer program 32, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the units 31 to 33 shown in fig. 3.
Illustratively, the computer program 32 may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 32 in the terminal 3.
The terminal 3 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal may include, but is not limited to, a processor 30, a memory 31. It will be appreciated by those skilled in the art that fig. 3 is only an example of a terminal 3 and does not constitute a limitation of the terminal 3 and may comprise more or less components than those shown, or some components may be combined, or different components, e.g. the terminal may further comprise input output devices, network access devices, buses, etc.
The Processor 30 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or a memory of the terminal 3. The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the terminal 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal 3. The memory 31 is used for storing the computer program and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (8)
1. A method for detecting the defect thickness of a thin layer of a multilayer concrete structure is characterized by comprising the following steps:
acquiring an echo signal, wherein the echo signal is a signal returned by a ground penetrating radar for detecting a concrete structure;
preprocessing the echo signal by utilizing fractional order S transformation to strengthen signal characteristics corresponding to thin layer defects in the echo signal;
the preprocessing the echo signal by utilizing fractional order S transformation to strengthen the signal characteristics of the echo signal corresponding to the thin layer defect comprises the following steps:
determining fractional factors in fractional order S transformation;
performing fractional order S transformation on the echo signal according to a preset fractional order S transformation formula, wherein the fractional order S transformation formula is as follows:
wherein, frST y (t, f, a) represents an echo signal after fractional order S conversion, Y a (τ) a fourier transform signal representing the echo signal, a representing a fractional factor;
the determining fractional factors in a fractional order S transform comprises:
calculating the cross entropy of the echo signal when only one signal is contained and when the echo signal contains an upper surface reflection signal and a lower surface reflection signal at the same time according to a preset cross entropy formula, wherein the cross entropy formula is as follows:
wherein the content of the first and second substances,
to represent
And
the cross-entropy between the two is,
representing an echo signal containing only one signal,
representing an echo signal containing both an upper surface reflection signal and a lower surface reflection signal;
taking a fraction factor which enables the cross entropy to appear at most as a determined fraction factor;
generating a gray image based on the preprocessed echo signal;
and inputting the gray level image into a trained thickness recognition model to obtain thickness information of the thin layer defect in the concrete structure.
2. The method for detecting the thickness of the thin layer defect of the multi-layer concrete structure according to claim 1, wherein before inputting the gray-scale image into the trained thickness recognition model, the method further comprises:
establishing an echo signal model base, wherein the echo signal model base comprises echo signals corresponding to multiple layers of concrete with different thin layer defect thicknesses;
and training the thickness recognition model through the echo signal model library to obtain the trained thickness recognition model.
3. The method for detecting the defect thickness of the thin layer of the multilayer concrete structure as claimed in claim 2, wherein the establishing of the echo signal model library comprises:
acquiring an echo signal for establishing an echo signal model library, wherein the echo signal for establishing the echo signal model library is a signal returned by a ground penetrating radar for detecting a concrete structure with known thickness information of a thin layer defect;
preprocessing the echo signals for establishing an echo signal model library by utilizing fractional order S transformation so as to strengthen signal characteristics corresponding to thin layer defects in the echo signals for establishing the echo signal model library;
and taking the processed echo signals for establishing an echo signal model library and the thickness information of the corresponding thin layer defects as data in the echo signal model library.
4. The method for detecting the defect thickness of the thin layer of the multilayer concrete structure according to claim 3, wherein the step of generating a gray scale image based on the preprocessed echo signals comprises the following steps:
calculating a gray level image corresponding to the preprocessed echo signal through a preset gray level image conversion formula, wherein the gray level image conversion formula is as follows:
wherein, I represents a gray-scale image,
the absolute value of the preprocessed echo signal is shown, and t and f represent the plane where the S transformation is located.
5. A device for detecting the defect thickness of a thin layer of a multilayer concrete structure is characterized by comprising:
the signal acquisition module is used for acquiring echo signals, wherein the echo signals are returned by a ground penetrating radar for detecting the concrete structure;
the characteristic strengthening module is used for preprocessing the echo signal by utilizing fractional order S transformation so as to strengthen the signal characteristics corresponding to the thin layer defects in the echo signal;
the image conversion module is used for generating a gray image based on the preprocessed echo signal;
the thickness analysis module is used for inputting the gray level image into a trained thickness recognition model to obtain thickness information of the thin layer defect in the concrete structure;
the feature augmentation module further comprises:
a fractional factor determination unit for determining a fractional factor in fractional order S-transform;
the fractional order S transformation unit is used for performing fractional order S transformation on the echo signal according to a preset fractional order S transformation formula, wherein the fractional order S transformation formula is as follows:
wherein, frST y (t, f, a) represents an echo signal after fractional order S conversion, Y a (τ) a fourier transform signal representing the echo signal, a representing a fractional factor;
the score factor determination unit is specifically configured to:
calculating the cross entropy of the echo signal when only containing one signal and the echo signal simultaneously contains the upper surface reflection signal and the lower surface reflection signal according to a preset cross entropy formula, wherein the cross entropy formula is as follows:
wherein the content of the first and second substances,
to represent
And
the cross-entropy between the two is,
representing an echo signal containing only one signal,
is shown as simultaneously containing the upper surfaceEcho signals of the reflected signals and the lower surface reflected signals;
and taking the fraction factor which enables the cross entropy to appear at most a plurality of minimum values as the determined fraction factor.
6. The apparatus for detecting the defect thickness of a thin layer of a multi-layered concrete structure according to claim 5, further comprising:
the model base establishing module is used for establishing an echo signal model base before inputting the gray level image into a trained thickness recognition model, wherein the echo signal model base comprises echo signals corresponding to multiple layers of concrete with different thin layer defect thicknesses;
and the model training module is used for training the thickness recognition model through the echo signal model library before inputting the gray level image into the trained thickness recognition model to obtain the trained thickness recognition model.
7. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method for detecting the defect thickness of thin layers of a multi-layer concrete structure as claimed in any one of the preceding claims 1 to 4.
8. A computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method for detecting the defect thickness of a thin layer of a multi-layer concrete structure according to any one of the preceding claims 1 to 4.
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