A three-dimensional imaging method and system for termite nests on embankments based on ground penetrating radar

Abstract

The present application discloses a method and system for three-dimensional imaging of termite nests on embankments based on ground-penetrating radar. The method comprises: acquiring three-dimensional ground-penetrating radar data of a target area and performing data preprocessing to obtain preprocessed three-dimensional ground-penetrating radar data; performing feature extraction and encryption processing on the preprocessed three-dimensional ground-penetrating radar data to obtain a multi-channel radar data volume; constructing a three-dimensional deep learning network model for termite nest imaging based on a U-shaped network structure; performing data imaging processing on the multi-channel radar data volume based on the three-dimensional deep learning network model for ant nest imaging to obtain an ant nest imaging result based on three-dimensional ground-penetrating radar. The embodiments of the present application can more accurately capture the complex features of ant nests and embankment structures in three-dimensional ground-penetrating radar images, thereby improving the accuracy of ant nest imaging. The present application can be widely used in the field of termite nest detection technology.

Description

Technical Field

[0001] The present application relates to the technical field of termite nest detection, and in particular to a three-dimensional imaging method and system for termite nests on embankments based on ground penetrating radar. Background Art

[0002] Due to the concealed nature and complex structure of termite nests, detecting termite nests on dams has always been a challenge in water conservancy projects. Currently, termite nest detection is primarily divided into three methods: manual exploration, geophysical exploration, and biological detection. Three-dimensional radar detection, which uses multiple transmitting and receiving antennas to generate high-resolution images of underground structures, offers advantages in termite nest detection. Through multi-frequency scanning, 3D radar can acquire three-dimensional data of the dam interior. However, current 3D radar data processing methods have limitations. First, the large volume of 3D data makes processing time-consuming and complex. Furthermore, 3D radar technology captures three-dimensional electromagnetic wave reflection signals, which appear as waveform features rather than the object's morphology. Consequently, current 3D radar imaging methods based on clustering or thresholding often produce inaccurate 3D images of ant nests.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to propose a three-dimensional imaging method and system for termite nests on dams based on ground-penetrating radar, which can more accurately determine the location and shape of the ant nest and capture the complex features of the ant nest and dam structure on the three-dimensional ground-penetrating radar image, thereby improving the efficiency and accuracy of ant nest imaging.

[0005] To achieve the above objectives, one aspect of the present application provides a method for three-dimensional imaging of termite nests on embankments based on ground penetrating radar, the method comprising:

[0006] Acquiring three-dimensional ground penetrating radar data of a target area and performing data preprocessing to obtain preprocessed three-dimensional ground penetrating radar data;

[0007] Performing feature extraction and encryption processing on the pre-processed three-dimensional ground penetrating radar data to obtain a multi-channel radar data volume;

[0008] Based on the U-shaped network structure, a three-dimensional deep learning network model for ant nest imaging was constructed;

[0009] The multi-channel radar data volume is subjected to data imaging processing based on the ant nest imaging three-dimensional deep learning network model to obtain a three-dimensional ground penetrating radar ant nest imaging result.

[0010] In some embodiments, acquiring three-dimensional ground-penetrating radar data of a target area and performing data preprocessing to obtain preprocessed three-dimensional ground-penetrating radar data includes:

[0011] determining a target area, scanning the target area using a three-dimensional radar to obtain a two-dimensional radar image of the target area;

[0012] Combining the two-dimensional radar images of the target area in a direction perpendicular to the survey line to obtain three-dimensional ground penetrating radar data of the target area;

[0013] Data preprocessing is performed on the three-dimensional ground penetrating radar data of the target area to obtain the preprocessed three-dimensional ground penetrating radar data.

[0014] In some embodiments, preprocessing the three-dimensional ground penetrating radar data of the target area to obtain the preprocessed three-dimensional ground penetrating radar data includes:

[0015] Performing zero-time correction processing on the three-dimensional ground penetrating radar data of the target area to obtain corrected three-dimensional ground penetrating radar data;

[0016] performing signal gain processing on the corrected three-dimensional ground penetrating radar data to obtain gained three-dimensional ground penetrating radar data;

[0017] The three-dimensional ground penetrating radar data after gain is filtered and background noise is removed by a bandpass filter to obtain the pre-processed three-dimensional ground penetrating radar data.

[0018] In some embodiments, performing feature extraction and encryption on the pre-processed three-dimensional ground penetrating radar data to obtain a multi-channel radar data volume includes:

[0019] Performing Hilbert transform analysis on the pre-processed three-dimensional ground penetrating radar data to obtain radar instantaneous frequency;

[0020] performing clustering processing on the pre-processed three-dimensional ground penetrating radar data by using a k-means clustering method to obtain radar clustering data;

[0021] performing an offset process on the pre-processed three-dimensional ground penetrating radar data to obtain radar offset data;

[0022] The preprocessed three-dimensional ground penetrating radar data, the radar instantaneous frequency, the radar clustering data and the radar offset data are combined and interpolated and encrypted to obtain the multi-channel radar data volume.

[0023] In some embodiments, performing offset processing on the pre-processed three-dimensional ground penetrating radar data to obtain radar offset data includes:

[0024] Converting the preprocessed three-dimensional ground penetrating radar data in a time-space domain into a frequency-wavenumber domain to obtain converted three-dimensional ground penetrating radar data;

[0025] determining frequency wavenumber information of the converted three-dimensional ground penetrating radar data according to the converted three-dimensional ground penetrating radar data;

[0026] Performing offset compensation on the frequency wavenumber information to obtain corrected frequency wavenumber domain data;

[0027] The corrected frequency wavenumber domain data is subjected to inverse two-dimensional Fourier transform and converted into time and space domain processing to obtain the radar offset data.

[0028] In some embodiments, the ant nest imaging three-dimensional deep learning network model includes several three-dimensional convolution modules and a deep neural network structure, and the several three-dimensional convolution modules and the deep neural network structure constitute a U-shaped network structure, wherein the three-dimensional convolution module includes a three-dimensional convolution layer, a batch normalization layer and an activation function, and the deep neural network structure includes a data input module, an encoder, a decoder, a jump connection and a convolution output module.

[0029] In some embodiments, the loss function of the ant nest imaging three-dimensional deep learning network model is specifically as follows:

[0030] Loss=BCE+Focal

[0031] BCE(p,y)=-[y·log(p)+(1-y)·log(1-p)]

[0032] Focal(p t )=-α t (1-p t ) 2 log(p t )

[0033] In the above formula, Loss represents the ant nest imaging three-dimensional deep learning network model, BCE(·) represents the cross entropy loss function, Focal(·) represents the focal loss function, y represents the label, p represents the probability of predicting the positive class, and p t represents the modulation factor, α t Represents a constant.

[0034] In some embodiments, performing data imaging processing on the multi-channel radar data volume based on the ant nest imaging three-dimensional deep learning network model to obtain a three-dimensional ground penetrating radar ant nest imaging result includes:

[0035] Inputting the multi-channel radar data volume into the ant nest imaging three-dimensional deep learning network model;

[0036] Based on the three-dimensional convolution module of the ant nest imaging three-dimensional deep learning network model, the multi-channel radar data volume is automatically subjected to feature extraction processing to obtain multi-channel radar feature data;

[0037] Based on the deep neural network structure of the ant nest imaging three-dimensional deep learning network model, data imaging processing is performed on the multi-channel radar feature data to obtain the three-dimensional ground penetrating radar ant nest imaging result.

[0038] In some embodiments, the deep neural network structure based on the ant nest imaging three-dimensional deep learning network model performs data imaging processing on the multi-channel radar feature data to obtain the three-dimensional ground penetrating radar ant nest imaging result, including:

[0039] Inputting the multi-channel radar feature data into the deep neural network structure of the ant nest imaging three-dimensional deep learning network model;

[0040] Acquire the multi-channel radar feature data based on the data input module of the deep neural network structure;

[0041] Based on the encoder of the deep neural network structure, the multi-channel radar feature data is subjected to feature space resolution reduction processing to obtain reduced multi-channel radar feature data;

[0042] Based on the decoder of the deep neural network structure, a spatial resolution recovery process is performed on the reduced multi-channel radar feature data to obtain restored multi-channel radar feature data;

[0043] Based on the skip connection of the deep neural network structure, the reduced multi-channel radar feature data and the restored multi-channel radar feature data are fused and down-sampled to obtain fused multi-channel radar feature data;

[0044] Based on the convolution output module of the deep neural network structure, the fused multi-channel radar feature data is classified into two categories to obtain the three-dimensional ground penetrating radar ant nest imaging result.

[0045] To achieve the above objectives, another aspect of the present application provides a three-dimensional imaging system for termite nests on embankments based on ground penetrating radar, the system comprising:

[0046] The first module is used to acquire three-dimensional ground penetrating radar data of the target area and perform data preprocessing to obtain preprocessed three-dimensional ground penetrating radar data;

[0047] The second module is used to perform feature extraction and encryption processing on the pre-processed three-dimensional ground penetrating radar data to obtain a multi-channel radar data volume;

[0048] The third module is used to build a three-dimensional deep learning network model for ant nest imaging based on a U-shaped network structure;

[0049] The fourth module is used to perform data imaging processing on the multi-channel radar data volume based on the ant nest imaging three-dimensional deep learning network model to obtain a three-dimensional ground penetrating radar ant nest imaging result.

[0050] The embodiments of the present application include at least the following beneficial effects: The present application provides a three-dimensional imaging method and system for termite nests on embankments based on ground-penetrating radar. The scheme obtains three-dimensional ground-penetrating radar data of the target area and performs data preprocessing, and then performs feature extraction and encryption processing on the preprocessed three-dimensional ground-penetrating radar data to obtain a multi-channel radar data body, which can better identify the position of different medium interfaces and more accurately determine the position and shape of the ant nest. Based on the U-shaped network structure, a three-dimensional deep learning network model for ant nest imaging is constructed, and finally, data imaging processing is performed on the multi-channel radar data body, which can process the spatial and depth information of the ground-penetrating radar data and capture the complex features of the ant nest and embankment structure in the three-dimensional ground-penetrating radar image, thereby improving the efficiency and accuracy of ant nest imaging. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a method for three-dimensional imaging of termite nests on embankments based on ground penetrating radar provided in an embodiment of the present application;

[0052] Figure 2 1 is a schematic structural diagram of a three-dimensional imaging system for termite nests on embankments based on ground penetrating radar provided in an embodiment of the present application;

[0053] Figure 3 is a schematic diagram of three-dimensional radar data acquisition provided by an embodiment of the present application;

[0054] Figure 4 is a schematic diagram of three-dimensional radar data processing provided by an embodiment of the present application;

[0055] Figure 5 Schematic diagram of the structure of the three-dimensional convolution operation provided in the embodiment of the present application;

[0056] Figure 6 This is a schematic diagram of the structure of the ant nest imaging three-dimensional deep learning network model provided in an embodiment of the present application;

[0057] Figure 7 This is a schematic diagram of the ant nest imaging effect of the three-dimensional ground penetrating radar provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of systems and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0059] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0060] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0062] Reference Figure 1 , Figure 1 A flowchart of a method for three-dimensional imaging of termite nests on embankments based on ground penetrating radar is provided in an embodiment of the present invention. Figure 1 , the method comprises the following steps:

[0063] S100, acquiring three-dimensional ground penetrating radar data of a target area and performing data preprocessing to obtain preprocessed three-dimensional ground penetrating radar data;

[0064] It should be noted that, in some embodiments, step S100 may include:

[0065] S110, determining a target area, scanning the target area using a three-dimensional radar, and obtaining a two-dimensional radar image of the target area;

[0066] S120, combining the two-dimensional radar images of the target area in a direction perpendicular to the survey line to obtain three-dimensional ground penetrating radar data of the target area;

[0067] In steps S110 to S120 of some embodiments, a three-dimensional ground penetrating radar is used to scan the survey area, and the collected data is pre-processed and preliminarily imaged.

[0068] Specifically, the center frequency of a 3D radar is 470MHz. During detection, the main controller controls the antenna array to excite each transmitting antenna at different times, and the receiving antenna receives the reflected signal within the corresponding time window. The transmitting antenna of the 3D radar emits electromagnetic waves, which are reflected and refracted in different media. The reflection coefficient of the electromagnetic wave at the interface between different media is:

[0069]

[0070] In the above formula, R represents the reflection coefficient of electromagnetic waves at the interface of different media, ε1 represents the dielectric constant of the upper medium, and ε2 represents the dielectric constant of the lower medium.

[0071] like Figure 3 As shown, the dielectric constant difference between the ant nests and the dam's construction materials is significant, causing electromagnetic waves to reflect. Therefore, when a 3D radar scans above the dam, it receives electromagnetic waves reflected from the ant nests. Combining the 2D radar images generated by each transmitting and receiving antenna pair perpendicular to the survey line yields 3D ground-penetrating radar data.

[0072] S130 , performing data preprocessing on the three-dimensional ground penetrating radar data of the target area to obtain preprocessed three-dimensional ground penetrating radar data.

[0073] It should be noted that in this embodiment, the collected radar data is preprocessed. The purpose of the preprocessing is to remove noise in the radar data, improve signal quality, and highlight the reflected wave of the target object.

[0074] Wherein, step S130 further includes:

[0075] S131, performing zero-time correction processing on the three-dimensional ground penetrating radar data of the target area to obtain corrected three-dimensional ground penetrating radar data;

[0076] In this embodiment, S131, the electromagnetic waves emitted by the transmitting antenna need a certain distance to propagate to the ground. Therefore, the position where the data time window is set to 0 is not the ground. Therefore, zero-time correction is required to improve the accuracy of depth calculation. The calculation method is to discard the data above the ground phase axis.

[0077] S132, performing signal gain processing on the corrected three-dimensional ground penetrating radar data to obtain gained three-dimensional ground penetrating radar data;

[0078] In this embodiment, S132, the electromagnetic waves emitted by the radar will attenuate during propagation in the underground medium, resulting in a weak signal at depth. Therefore, it is necessary to perform gain processing on the radar data. Assume that the signal received by the radar receiving antenna is s(t), and a gain window T is specified. The RMS value of the signal at each time point t is:

[0079]

[0080] In the above formula, E(t) represents the RMS value of the signal at each time point t, T represents the gain window, t represents the time point, s represents the signal received by the receiving antenna, and τ represents the integral independent variable.

[0081] The root mean square of the signal at each time point t is the local energy of the signal. The gain factor is calculated based on the local energy of the signal, and its expression is:

[0082]

[0083] In the above formula, G(t) represents the gain factor, and ε is a very small positive number to avoid the denominator being zero.

[0084] S133 , filtering and removing background noise from the amplified three-dimensional ground penetrating radar data using a bandpass filter to obtain preprocessed three-dimensional ground penetrating radar data.

[0085] In this embodiment, in step S133, a bandpass filter is used to filter out interference waves received by the receiving antenna. Subsequently, all measured values ​​are summed horizontally using an overall averaging technique to obtain an average value, which is then subtracted from the initial data to eliminate background interference data.

[0086] S200, performing feature extraction and encryption processing on the pre-processed three-dimensional ground penetrating radar data to obtain a multi-channel radar data volume;

[0087] It should be noted that, in some embodiments, step S200 may include:

[0088] S210, performing Hilbert transform analysis on the pre-processed three-dimensional ground penetrating radar data to obtain the radar instantaneous frequency;

[0089] In this embodiment S210, the instantaneous frequency of the ground-penetrating radar refers to the frequency of the signal at a certain moment in the process of changing over time, reflecting the dynamic change of the signal frequency over time. The instantaneous frequency will change significantly in the transition layer or reflection interface of different media. By analyzing the instantaneous frequency, the layered structure or reflection interface of the underground medium can be located. Assuming that the received radar signal is s(t), its analytical signal is obtained through Hilbert transform, which is expressed as:

[0090] z(t)=s(t)+j·H[s(t)]

[0091] In the above formula, H[s(t)] is the Hilbert transform of the radar signal s(t), and z(t) is the complex representation of the analytical signal.

[0092] Furthermore, the instantaneous frequency f(t) is the derivative of the analytical signal phase φ(t) with respect to time, and its expression is:

[0093]

[0094] In the above formula, φ(t) = arg(z(t)) is the phase of the analytical signal. The instantaneous frequency of the two-dimensional radar data formed by each transmitting-receiving antenna pair of the three-dimensional radar is calculated, and the following is obtained: Figure 4 The data shown.

[0095] S220, clustering the preprocessed three-dimensional ground penetrating radar data using a k-means clustering method to obtain radar cluster data;

[0096] In this embodiment, S220, the k-means clustering method is used to process the three-dimensional radar data. The role of k-means clustering is to perform data pattern recognition and target classification on the radar data, which helps to extract the target echo in the radar signal. K-means clustering is performed on the two-dimensional radar data generated by each transmitting-receiving antenna pair of the three-dimensional radar to obtain the following: Figure 4 The data shown.

[0097] S230, performing offset processing on the pre-processed three-dimensional ground penetrating radar data to obtain radar offset data;

[0098] Wherein, step S230 further includes:

[0099] S231, converting the time-space domain of the pre-processed three-dimensional ground penetrating radar data into a frequency-wavenumber domain to obtain converted three-dimensional ground penetrating radar data;

[0100] S232, determining frequency wavenumber information of the converted three-dimensional ground penetrating radar data according to the converted three-dimensional ground penetrating radar data;

[0101] S233, performing offset compensation on the frequency wavenumber information to obtain corrected frequency wavenumber domain data;

[0102] S234. Perform inverse two-dimensional Fourier transform and conversion into time and space domain on the corrected frequency wavenumber domain data to obtain radar offset data.

[0103] In this embodiment, in S230, the three-dimensional radar data is subjected to an offset process. The purpose of the offset process is to correct the target's reflected echo to the target's true position. When performing the offset process, the radar data is first converted from the time-space domain (tx domain) to the frequency wavenumber domain (fk domain).

[0104]

[0105] In the above formula, d(x,t) is the radar data in the time and space domain, and D(k,f) represents the radar data in the wave number k and frequency f domain.

[0106] In the frequency-wavenumber domain, the relationship between frequency and wavenumber is expressed as:

[0107]

[0108] In the above formula, v is the speed of electromagnetic waves in the medium.

[0109] In the migration process, the offset wave is corrected to the correct position by using the known wave velocity information. This process involves compensating the frequency corresponding to each wave number accordingly. The compensation process is to calculate a new wave number k based on the given wave velocity v. new , whose expression is:

[0110]

[0111] New wave number k new That is the corrected wave number, the new frequency-wave number domain data D(k new ,f) Perform inverse two-dimensional Fourier transform and convert it back to the time and space domain to obtain the offset data d shift (x,t).

[0112]

[0113] In the above formula, d shifted (x, t) represents the data after offset, D(k new ,f) represents the new frequency-wavenumber domain data.

[0114] S240 , combining the pre-processed three-dimensional ground penetrating radar data, radar instantaneous frequency, radar clustering data, and radar offset data and performing interpolation encryption to obtain a multi-channel radar data volume.

[0115] In this embodiment, in S240, the instantaneous frequency, cluster data, and offset data of the calculated three-dimensional radar data are interpolated and encrypted to obtain a three-dimensional radar data volume of size 256*256*64 (track*time*line), as shown in FIG. Figure 4 shown.

[0116] In summary, the embodiments of the present invention not only process three-dimensional radar reflection data, but also combine multi-dimensional information such as instantaneous frequency, cluster data, and offset data. Data of different dimensions has different functions.

[0117] 1) Raw radar data: Data preprocessing (denoising and gain correction) removes noise interference and enhances signal quality. This is the reflected signal directly acquired by ground-penetrating radar, containing information about the propagation and reflection of electromagnetic waves in the subsurface. It reflects the electromagnetic reflection characteristics of different underground structures.

[0118] 2) Instantaneous frequency data: Instantaneous frequency reflects the frequency distribution of radar signals over time, explaining the propagation characteristics of electromagnetic waves in different media. At material interfaces, instantaneous frequency changes significantly, so the location of interfaces between different media can be better identified by using this variation in instantaneous frequency.

[0119] 3) Clustering data: K-means clustering is used to perform pattern recognition and classification on radar data. Different areas in the reflected signal are classified according to data characteristics, thereby separating targets (such as ant nests) from background signals.

[0120] 4) Offset data: By offsetting the position of the echo and adjusting the reflected signal from the recorded position to the actual position of the target, the position and shape of the ant nest can be determined more accurately, avoiding position errors caused by the wave propagation path.

[0121] The above data are encrypted through interpolation to form a multi-channel data volume, and the different information of the above data are fused in the network input, providing a more comprehensive ant nest feature representation, which helps to improve the accuracy of imaging.

[0122] S300, based on the U-shaped network structure, builds a 3D deep learning network model for ant nest imaging;

[0123] It should be noted that in some embodiments, 3D ground penetrating radar data is imaged by a 3D deep neural network. The deep neural network is composed of Figure 5 The three-dimensional convolution module is composed of the following components: Figure 6 The specific hierarchical network structure parameters of the “U”-shaped network shown in FIG. 1 are shown in Table 1.

[0124] The ant nest imaging three-dimensional deep learning network model includes several three-dimensional convolution modules and deep neural network structures. Several three-dimensional convolution modules and the deep neural network structure form a U-shaped network structure. Among them, the three-dimensional convolution module includes a three-dimensional convolution layer, a batch normalization layer and an activation function, and the deep neural network structure includes a data input module, an encoder, a decoder, a skip connection and a convolution output module.

[0125] In addition, it should be noted that traditional 3D GPR data processing methods rely on techniques such as clustering or thresholding, resulting in inaccurate imaging results. The embodiment of the present invention uses a 3D convolution module to process the spatial and depth information of GPR data. The 3D convolution network can capture the complex features of ant nests and dam structures in 3D GPR images, making it more suitable for 3D data processing than traditional 2D convolution. The 3D convolution neural network structure in the embodiment of the present invention, in which the 3D convolution module forms a "U"-shaped network structure, improves the accuracy and resolution of imaging by automatically extracting and modeling multi-channel 3D GPR data. The combination of skip connections preserves detail information, resolving the contradiction between high-level feature extraction and reduced spatial resolution.

[0126] Table 1 Network structure hierarchy data table

[0127]

[0128]

[0129] S400, performing data imaging processing on the multi-channel radar data volume based on the ant nest imaging three-dimensional deep learning network model to obtain a three-dimensional ground penetrating radar ant nest imaging result;

[0130] It should be noted that, in some embodiments, step S400 may include:

[0131] S410, inputting the multi-channel radar data volume into the ant nest imaging three-dimensional deep learning network model;

[0132] S420, performing automated feature extraction processing on the multi-channel radar data volume using a three-dimensional convolution module based on the ant nest imaging three-dimensional deep learning network model to obtain multi-channel radar feature data;

[0133] In this embodiment S420, the three-dimensional convolution module is used to process three-dimensional data with space and depth. The input of the three-dimensional convolution operation is (C, H, W, D), where C is the number of channels, H is the height, W is the width, and D represents the depth. The size of the three-dimensional convolution kernel is 3*3*3. During the calculation, the convolution kernel slides in the depth, height and width directions of the input. At each position, the convolution kernel and the corresponding part of the input are element-wise multiplied to obtain a numerical output. Each value of the output feature map is obtained by multiplying the convolution kernel by a three-dimensional neighborhood area of ​​the input data. Batch normalization and activation function operations are performed after each three-dimensional convolution operation. The batch normalization operation adjusts the output mean of each small batch of data to 0 and the variance to 1, thereby reducing the offset of the input features. The activation function layer consists of an activation function, defined as f(x)=max(0,x), that is, all negative values ​​are set to 0 and positive values ​​remain unchanged. By introducing the activation function, the linear transformation is converted into a more complex nonlinear mapping, thereby enhancing the expression ability of the network. Therefore, the structure of the 3D convolution module is "3D convolution + batch normalization + activation function".

[0134] S430. Based on the deep neural network structure of the ant nest imaging three-dimensional deep learning network model, data imaging processing is performed on the multi-channel radar feature data to obtain a three-dimensional ground penetrating radar ant nest imaging result.

[0135] Wherein, step S430 further includes:

[0136] S431, inputting the multi-channel radar feature data into the deep neural network structure of the ant nest imaging three-dimensional deep learning network model;

[0137] S432. A data input module based on a deep neural network structure acquires multi-channel radar feature data;

[0138] In this embodiment, S432, the network inputs are the interpolated and encrypted raw radar data volume, the instantaneous frequency volume, the clustered data volume, and the offset data volume. These data volumes form a multi-channel radar data volume with a shape of (B, 4, 256, 256, 64), where B is the batch size. These data volumes are first passed through a convolutional layer to extract preliminary low-level features, converting the data from the input channels into higher-level feature channels.

[0139] S433, performing feature space resolution reduction processing on the multi-channel radar feature data based on an encoder with a deep neural network structure to obtain reduced multi-channel radar feature data;

[0140] In this embodiment, S433, the encoder is used to gradually extract features from the input data, compressing the original data into a higher-dimensional feature representation. The encoder reduces the spatial resolution of the features through downsampling (implemented by maximum pooling) and extracts more complex and abstract features through multi-layer convolution.

[0141] The encoder's feature extraction function is implemented through three-dimensional convolution. Data passes through the convolutional layer to extract high-level features, while the number of channels in the data gradually increases. After the three-dimensional convolution, the data undergoes a downsampling operation. Downsampling reduces the spatial dimension of the data through maximum pooling, aiming to reduce the data's resolution while retaining important global information.

[0142] A data body of size (B, C, H, W, D) (where B is the batch size, C is the number of channels, H is the height, W is the width, and D is the data depth) becomes (B, 2C, H, W, D) after the convolution operation, and becomes (B, 2C, H / 2, W / 2, D / 2) after the convolution operation and the downsampling operation.

[0143] S434. A decoder based on a deep neural network structure performs spatial resolution restoration processing on the reduced multi-channel radar feature data to obtain restored multi-channel radar feature data.

[0144] In this embodiment, S434, the decoder's role is to gradually restore the spatial resolution, converting the high-level feature maps output by the encoder back to the size of the original input data while generating accurate predictions. The decoder gradually increases the spatial dimensions of the feature maps through upsampling (implemented via bilinear interpolation) and fuses local details with global features through convolutional layers, incorporating skip connection information from the encoder.

[0145] The decoder performs decoding via 3D convolution. The input to the 3D convolution layer is the upsampled features and the features concatenated from the corresponding encoder layers via skip connections. The convolved data undergoes a single upsampling operation, which restores the spatial dimensions of the feature map to the original input size through bilinear interpolation.

[0146] A data volume of size (B, C, H, W, D) becomes (B, C / 2, H, W, D) after convolution operation, and becomes (B, C / 2, 2*H, 2*W, 2*D) after convolution operation and upsampling operation.

[0147] S435. Based on a skip connection of a deep neural network structure, fuse and downsample the reduced multi-channel radar feature data and the restored multi-channel radar feature data to obtain fused multi-channel radar feature data;

[0148] In this embodiment, S435, the skip connection serves to fuse low-level information from the encoder with high-level information from the decoder. While the encoder extracts high-level semantic features through multiple downsampling operations, the spatial resolution gradually decreases, resulting in the loss of low-level information. These low-level features contain details such as boundaries and textures. The skip connection directly transfers the high-resolution low-level features from the encoder to the corresponding layers of the decoder, where they are combined with the features in the decoder to help recover detailed information. These low-level features contain details such as boundaries and textures.

[0149] S436, based on the convolution output module of the deep neural network structure, performs binary classification on the fused multi-channel radar feature data to obtain the three-dimensional ground penetrating radar ant nest imaging results.

[0150] In this embodiment, in S436, after all the upsampling and convolution processing in the decoder, the network passes through a final convolution layer to compress the multi-channel feature map to the required number of channels. Subsequently, the features output by the convolution layer are processed by the activation function, which converts the final output result into a binary classification result, with the ant nest target body as 1 and the disease-free dam body as 0, as shown in Figure 4. Figure 7 shown.

[0151] The activation function is:

[0152]

[0153] In the above formula, f(x) represents the activation function.

[0154] The input of the network is a multi-channel data volume with a shape of (B, 4, 256, 256, 64). After the final convolution processing, the output data volume of the network has a shape of (B, 1, 256, 256, 64), which is a single-channel data representing the spatial characteristics of the target volume.

[0155] In addition, it should be noted that the input data of the network are the original radar data volume, the instantaneous frequency volume, the clustering data volume and the offset data volume. The input is denoted as input, the output of the network is denoted as output, and the three-dimensional deep neural network is denoted as F. Then the output of the network is:

[0156] output=F(input)

[0157] The output of the network is binary classification data. The label of each voxel in the real model is y, where y takes the value of 0 or 1, representing the dam background and the ant nest target respectively. The probability of the output of the binary classification data volume output by the network being predicted as the positive class is p. The output of the network is compared with the actual model to calculate the cross entropy error, which is expressed as:

[0158] BCE(p,y)=-[y·log(p)+(1-y)·log(1-p)]

[0159] At the same time, most of the network output is the area without any target objects, and only a small area contains the area with ant nest targets, so there is a large class imbalance problem. Focus loss is used to reduce this imbalance problem.

[0160] The formula of focal loss is:

[0161] Focal(p t )=-α t (1-p t ) 2 log(p t )

[0162] In the above formula (1-p t ) 2 is the modulation factor, where p t for:

[0163]

[0164] When p t The closer it is to 1, the closer the modulation factor will be to 0, and the loss of easy-to-classify samples will be greatly reduced; when p t The closer it is to 0, the closer the modulation factor will be to 1, and the model will pay more attention to difficult-to-classify samples. t The expression is:

[0165]

[0166] In the above formula, α is set to 0.75 to reduce the dominant effect of negative samples on the overall loss. The final network loss is a combination of cross entropy loss and focal loss, and its expression is:

[0167] Loss=BCE+Focal

[0168] In the above formula, Loss represents the ant nest imaging three-dimensional deep learning network model, BCE(·) represents the cross entropy loss function, and Focal(·) represents the focal loss function.

[0169] Network training optimizes the above loss function to improve the network's prediction performance.

[0170] In summary, the embodiments of the present invention perform three-dimensional radar detection on the dam and collect three-dimensional ground-penetrating radar data; perform data preprocessing on the collected three-dimensional ground-penetrating radar data; further process the preprocessed three-dimensional ground-penetrating radar data to extract its instantaneous frequency, clustering and offset data; interpolate and encrypt the above data to form a multi-channel radar data body; construct a three-dimensional deep learning network and perform network training optimization, and optimize the network parameters by optimizing the loss function; input the measured data into the neural network and perform imaging, which can process the spatial and depth information of the ground-penetrating radar data and capture the complex features of ant nests and dam structures in the three-dimensional ground-penetrating radar images, thereby improving the efficiency and accuracy of ant nest imaging.

[0171] See also Figure 2 The present application also provides a ground-penetrating radar-based three-dimensional imaging system for termite nests on dams, which can implement the above-mentioned ground-penetrating radar-based three-dimensional imaging method for termite nests on dams. The system includes:

[0172] The first module 201 is used to acquire 3D ground penetrating radar data of a target area and perform data preprocessing to obtain preprocessed 3D ground penetrating radar data;

[0173] The second module 202 is used to perform feature extraction and encryption processing on the pre-processed 3D ground penetrating radar data to obtain a multi-channel radar data volume;

[0174] The third module 203 is used to build an ant nest imaging three-dimensional deep learning network model based on a U-shaped network structure;

[0175] The fourth module 204 is used to perform data imaging processing on the multi-channel radar data volume based on the ant nest imaging three-dimensional deep learning network model to obtain a three-dimensional ground penetrating radar ant nest imaging result.

[0176] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0177] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A three-dimensional imaging method for termite nests on embankments based on ground penetrating radar, characterized in that: The method comprises the following steps: Acquiring three-dimensional ground penetrating radar data of a target area and performing data preprocessing to obtain preprocessed three-dimensional ground penetrating radar data; Performing feature extraction and encryption processing on the pre-processed three-dimensional ground penetrating radar data to obtain a multi-channel radar data volume; Based on the U-shaped network structure, a three-dimensional deep learning network model for ant nest imaging was constructed; Performing data imaging processing on the multi-channel radar data volume based on the ant nest imaging three-dimensional deep learning network model to obtain a three-dimensional ground penetrating radar ant nest imaging result; The step of performing feature extraction and encryption on the pre-processed three-dimensional ground penetrating radar data to obtain a multi-channel radar data volume includes: Performing Hilbert transform analysis on the pre-processed three-dimensional ground penetrating radar data to obtain radar instantaneous frequency; performing clustering processing on the pre-processed three-dimensional ground penetrating radar data by using a k-means clustering method to obtain radar clustering data; performing an offset process on the pre-processed three-dimensional ground penetrating radar data to obtain radar offset data; Combining the preprocessed three-dimensional ground penetrating radar data, the radar instantaneous frequency, the radar clustering data, and the radar offset data and performing interpolation encryption to obtain the multi-channel radar data volume; The performing data imaging processing on the multi-channel radar data volume based on the ant nest imaging three-dimensional deep learning network model to obtain a three-dimensional ground penetrating radar ant nest imaging result includes: Inputting the multi-channel radar data volume into the ant nest imaging three-dimensional deep learning network model; Based on the three-dimensional convolution module of the ant nest imaging three-dimensional deep learning network model, the multi-channel radar data volume is automatically subjected to feature extraction processing to obtain multi-channel radar feature data; Based on the deep neural network structure of the ant nest imaging three-dimensional deep learning network model, the multi-channel radar feature data is subjected to data imaging processing to obtain the three-dimensional ground penetrating radar ant nest imaging result; The deep neural network structure based on the ant nest imaging three-dimensional deep learning network model performs data imaging processing on the multi-channel radar feature data to obtain the three-dimensional ground penetrating radar ant nest imaging result, including: Inputting the multi-channel radar feature data into the deep neural network structure of the ant nest imaging three-dimensional deep learning network model; The data input module based on the deep neural network structure obtains the multi-channel radar feature data; the network input is the original radar data volume, instantaneous frequency volume, cluster data volume and offset data volume after interpolation encryption, and multiple data volumes constitute a multi-channel radar data volume; Based on the encoder of the deep neural network structure, the multi-channel radar feature data is subjected to feature space resolution reduction processing to obtain reduced multi-channel radar feature data; Based on the decoder of the deep neural network structure, a spatial resolution recovery process is performed on the reduced multi-channel radar feature data to obtain restored multi-channel radar feature data; Based on the skip connection of the deep neural network structure, the reduced multi-channel radar feature data and the restored multi-channel radar feature data are fused and down-sampled to obtain fused multi-channel radar feature data; Based on the convolution output module of the deep neural network structure, the fused multi-channel radar feature data is classified into two categories to obtain the three-dimensional ground penetrating radar ant nest imaging result.

2. The method according to claim 1, characterized in that The method of acquiring three-dimensional ground penetrating radar data of a target area and performing data preprocessing to obtain preprocessed three-dimensional ground penetrating radar data includes: determining a target area, scanning the target area using a three-dimensional radar to obtain a two-dimensional radar image of the target area; Combining the two-dimensional radar images of the target area in a direction perpendicular to the survey line to obtain three-dimensional ground penetrating radar data of the target area; Data preprocessing is performed on the three-dimensional ground penetrating radar data of the target area to obtain the preprocessed three-dimensional ground penetrating radar data.

3. The method according to claim 2, characterized in that The preprocessing of the three-dimensional ground penetrating radar data of the target area to obtain the preprocessed three-dimensional ground penetrating radar data includes: Performing zero-time correction processing on the three-dimensional ground penetrating radar data of the target area to obtain corrected three-dimensional ground penetrating radar data; performing signal gain processing on the corrected three-dimensional ground penetrating radar data to obtain gained three-dimensional ground penetrating radar data; The three-dimensional ground penetrating radar data after gain is filtered and background noise is removed by a bandpass filter to obtain the pre-processed three-dimensional ground penetrating radar data.

4. The method according to claim 1, wherein The performing offset processing on the pre-processed three-dimensional ground penetrating radar data to obtain radar offset data includes: Converting the preprocessed three-dimensional ground penetrating radar data in a time-space domain into a frequency-wavenumber domain to obtain converted three-dimensional ground penetrating radar data; determining frequency wavenumber information of the converted three-dimensional ground penetrating radar data according to the converted three-dimensional ground penetrating radar data; Performing offset compensation on the frequency wavenumber information to obtain corrected frequency wavenumber domain data; The corrected frequency wavenumber domain data is subjected to inverse two-dimensional Fourier transform and converted into time and space domain processing to obtain the radar offset data.

5. The method according to claim 1, characterized in that The ant nest imaging three-dimensional deep learning network model includes several three-dimensional convolution modules and a deep neural network structure. The several three-dimensional convolution modules and the deep neural network structure constitute a U-shaped network structure, wherein the three-dimensional convolution module includes a three-dimensional convolution layer, a batch normalization layer and an activation function, and the deep neural network structure includes a data input module, an encoder, a decoder, a skip connection and a convolution output module.

6. The method according to claim 5, characterized in that The loss function of the ant nest imaging three-dimensional deep learning network model is specifically as follows: In the above formula, represents the ant nest imaging 3D deep learning network model, represents the cross entropy loss function, represents the focal loss function, Indicates a label, represents the probability of predicting the positive class, represents the modulation factor, Represents a constant.

7. A three-dimensional imaging system for termite nests on embankments based on ground penetrating radar, characterized in that: The system comprises: The first module is used to acquire three-dimensional ground penetrating radar data of the target area and perform data preprocessing to obtain preprocessed three-dimensional ground penetrating radar data; The second module is used to perform feature extraction and encryption processing on the pre-processed three-dimensional ground penetrating radar data to obtain a multi-channel radar data volume; The third module is used to build a three-dimensional deep learning network model for ant nest imaging based on a U-shaped network structure; A fourth module is configured to perform data imaging processing on the multi-channel radar data volume based on the ant nest imaging three-dimensional deep learning network model to obtain a three-dimensional ground penetrating radar ant nest imaging result; The step of performing feature extraction and encryption on the pre-processed three-dimensional ground penetrating radar data to obtain a multi-channel radar data volume includes: Performing Hilbert transform analysis on the pre-processed three-dimensional ground penetrating radar data to obtain radar instantaneous frequency; performing clustering processing on the pre-processed three-dimensional ground penetrating radar data by using a k-means clustering method to obtain radar clustering data; performing an offset process on the pre-processed three-dimensional ground penetrating radar data to obtain radar offset data; Combining the preprocessed three-dimensional ground penetrating radar data, the radar instantaneous frequency, the radar clustering data, and the radar offset data and performing interpolation encryption to obtain the multi-channel radar data volume; The performing data imaging processing on the multi-channel radar data volume based on the ant nest imaging three-dimensional deep learning network model to obtain a three-dimensional ground penetrating radar ant nest imaging result includes: Inputting the multi-channel radar data volume into the ant nest imaging three-dimensional deep learning network model; Based on the three-dimensional convolution module of the ant nest imaging three-dimensional deep learning network model, the multi-channel radar data volume is automatically subjected to feature extraction processing to obtain multi-channel radar feature data; Based on the deep neural network structure of the ant nest imaging three-dimensional deep learning network model, the multi-channel radar feature data is subjected to data imaging processing to obtain the three-dimensional ground penetrating radar ant nest imaging result; The deep neural network structure based on the ant nest imaging three-dimensional deep learning network model performs data imaging processing on the multi-channel radar feature data to obtain the three-dimensional ground penetrating radar ant nest imaging result, including: Inputting the multi-channel radar feature data into the deep neural network structure of the ant nest imaging three-dimensional deep learning network model; The data input module based on the deep neural network structure obtains the multi-channel radar feature data; the network input is the original radar data volume, instantaneous frequency volume, cluster data volume and offset data volume after interpolation encryption, and multiple data volumes constitute a multi-channel radar data volume; Based on the encoder of the deep neural network structure, the multi-channel radar feature data is subjected to feature space resolution reduction processing to obtain reduced multi-channel radar feature data; Based on the decoder of the deep neural network structure, a spatial resolution recovery process is performed on the reduced multi-channel radar feature data to obtain restored multi-channel radar feature data; Based on the skip connection of the deep neural network structure, the reduced multi-channel radar feature data and the restored multi-channel radar feature data are fused and down-sampled to obtain fused multi-channel radar feature data; Based on the convolution output module of the deep neural network structure, the fused multi-channel radar feature data is classified into two categories to obtain the three-dimensional ground penetrating radar ant nest imaging result.

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