Ground penetrating radar time-frequency feature selection method and system based on crack form similarity

Abstract

The invention discloses a ground penetrating radar time-frequency feature selection method and system based on crack form similarity, and belongs to the technical field of road engineering pavement maintenance. The method comprises the following steps: performing time-frequency characteristic analysis on an extracted electromagnetic signal; the change rate of the crack and the change rate of the complete benefit are calculated, and the change rate calculated by each piece of data in the crack marking frame is mapped to the crack identification marking frame; further calculating the maximum curvature value of the change rate curve, and forming a mark point; and analyzing the morphological similarity of the mark points and the cracks to obtain a feature comprehensive score, and determining the feature importance. According to the method, feature screening is carried out based on the similarity between the crack form obtained through feature recognition and the actual crack form, the sensitivity of different features to the crack can be visually indicated, the feature most sensitive to the crack is screened out, and the number of the features can be greatly reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of pavement maintenance of road engineering, and particularly relates to a ground penetrating radar time-frequency feature selection method and system based on crack morphology similarity. BACKGROUND

[0002] The structure type commonly used in high-grade highways is semi-rigid base asphalt pavement. The semi-rigid base has certain slab body and stability. However, the semi-rigid base also has inherent shortcomings: under the action of environmental factors such as dry and wet and freeze-thaw cycles and repeated loads, cracks are extremely easy to occur. The cracks damage the integrity and continuity of the pavement structure and to some extent weaken the structural strength. The occurrence of cracks also reduces the waterproofness of the pavement system. Most of the water entering the pavement structure through the cracks is retained between the surface layer and the base layer due to the lack of proper drainage channels. Under the repeated action of driving loads, a large amount of dynamic water pressure is generated to scour the base layer, widen the cracks and cause the asphalt pavement on both sides of the cracks to be bitten. The repeated action of this process eventually causes the base layer to lose support and connection with the surface layer, thereby causing the asphalt surface layer to appear cracks and other damages, accelerating the damage of the asphalt pavement and affecting the quality and service life of the highway. Therefore, during the service of the semi-rigid base asphalt pavement, crack detection work needs to be carried out regularly to comprehensively understand the development process of the cracks on the pavement.

[0003] In the traditional technology, the crack detection methods mainly include drilling sampling, falling weight deflectometry and ultrasonic detection. This type of detection method has the problems of strong destructiveness, low efficiency, great influence of environment, etc., and is difficult to meet the needs of modern road maintenance for efficient and accurate detection. In comparison, the ground penetrating radar (GPR) can realize continuous non-destructive detection at different depths due to its non-contact, high efficiency and high resolution, and has been widely applied in the fields of bridges, railways, tunnels, roads, etc. Then, in the identification of road crack diseases, feature selection is a key link that affects the prediction accuracy of GPR data. In addition, the GPR data of cracks generally has the characteristics of multi-dimension and high complexity, and the data scale is large, the nonlinear interaction between features is significant, the noise interference is serious, and the sample distribution is relatively discrete. Therefore, how to select reasonable features that match the cracks from the large-scale GPR data to improve the efficiency and accuracy of GPR detection is a problem that needs to be solved at present. SUMMARY

[0004] The application discloses a ground penetrating radar time-frequency feature selection method and system based on crack morphology similarity. The similarity between the crack morphology identified by the features and the actual crack morphology is used for screening the features, which can intuitively indicate the sensitivity of different features to cracks, screen out the features most sensitive to cracks, and greatly reduce the number of features, thereby effectively solving at least one technical problem involved in the background technology.

[0005] To achieve the above object, the technical scheme of the present application is: A ground penetrating radar time-frequency feature selection method based on crack morphology similarity, comprising the following steps: Step S1, obtaining the ground penetrating radar detection result of the road surface, taking the road surface area corresponding to the radar slice graph of the crack annotation box as the target area, and obtaining the radar electromagnetic signal of the target area, wherein the radar electromagnetic signal of the target area includes the radar electromagnetic signal of the crack position and the intact position; Step S2, performing time-frequency feature analysis on the radar electromagnetic signal extracted in step S1, and extracting a plurality of time domain features and frequency domain features respectively; Step S3, taking any one time-frequency feature as a target feature, obtaining the feature values of the target area under different channels and calculating the feature difference value and the feature change rate, and taking the ratio of the maximum value and the minimum value of the three analysis indexes under all channels as the scaling coefficient; wherein the feature difference value represents the feature difference value of the crack position and the intact position, and the feature change rate represents the ratio of the feature difference value and the feature value; Step S4, selecting the index with the maximum scaling coefficient among the three analysis indexes as the optimal analysis index of the target feature, mapping the optimal analysis index value of the target feature to the radar slice graph, and marking the position corresponding to the maximum value of the optimal analysis index on the radar slice graph; Step S5, calculating the horizontal distance between the marked point and the crack position and the variance of the horizontal distance, taking the weighted sum of the horizontal distance and the variance of the horizontal distance as the evaluation index, calculating the score of the target feature, and traversing all time domain features and frequency domain features to obtain the scores of all time-frequency features; Step S6, sorting all time-frequency feature scores from high to low, selecting a number of time-frequency features with high scores as the preliminary screening features, and then statistically analyzing the preliminary screening features of a plurality of target areas, counting the number of each preliminary screening feature appearing under all target areas, and sorting the preliminary screening features from high to low according to the number of appearances, and selecting a number of preliminary screening features with high ranking as the final analysis features.

[0006] As a preferred improvement, the crack annotation box is obtained by the YOLOv8 model performing crack recognition on the radar slice graph.

[0007] As a preferred improvement, the process of obtaining the radar electromagnetic signal of the target area specifically comprises the following steps: Step S11, obtaining the name of the radar slice graph where the crack annotation box is located; Step S12, according to the naming rule of the radar slice graph, extracting the trace number range of the radar slice graph through the name determined in step S11; Step S13, according to the lane number range determined in step S12, the radar electromagnetic signal of the target area is extracted from the ground penetrating radar detection result.

[0008] As a preferred improvement, the time domain features include mean, absolute mean, maximum, square difference, cubic difference, quartic difference, root mean square, standard deviation, peak-to-peak value, square of mean square value, Peak-Po-RMS, skewness, kurtosis, Max-Po-mean, RMS-Po-abs, Max-Po-abs, energy, power, waveform factor, peak factor, impact factor, zero-crossing rate, average amplitude difference, weighted average, dynamic range, instantaneous mean difference, short-time power change rate, amplitude non-stationarity, autocorrelation, extreme value number; the frequency domain features include spectral mean, spectral standard deviation, spectral skewness, spectral kurtosis, weighted frequency average, weighted mean square difference, weighted variance, fourth central moment, peak factor, skewness ratio 9, third kurtosis, fourth kurtosis, low-frequency energy ratio, medium-frequency energy ratio, high-frequency energy ratio, spectral centroid, spectral entropy, spectral flatness, frequency variance, bandwidth, harmonic distortion rate, main frequency, spectral skewness, signal-to-noise ratio.

[0009] As a preferred improvement, the evaluation index is represented as: In the formula: is the comprehensive score, is the horizontal distance normalized value; is the horizontal distance variance normalized value; , respectively represent , the weight of.

[0010] As a preferred improvement, the preliminary screening features are the top 10 time-frequency features of each target area score; the final analysis features are the top 10 preliminary screening features in the order of the number of occurrences of all target areas.

[0011] A ground penetrating radar time-frequency feature selection system based on crack morphology similarity, comprising: A signal acquisition module is configured to obtain ground penetrating radar detection results of a road surface, take a road surface area corresponding to a radar slice graph of a crack annotation frame as a target area, and obtain radar electromagnetic signals of the target area, wherein the radar electromagnetic signals of the target area include radar electromagnetic signals of crack positions and intact positions; A feature analysis module is configured to perform time-frequency feature analysis on the extracted radar electromagnetic signals, and extract a plurality of time domain features and frequency domain features respectively; The analysis index construction module takes any one time-frequency feature as a target feature, obtains feature values of the target region under different channels and calculates feature difference values and feature change rates, and takes the ratio of the maximum value to the minimum value of the three analysis indexes, i.e., the feature value, the feature difference value and the feature change rate, under all channels as a scaling coefficient; wherein the feature difference value represents the feature difference value between the crack position and the intact position, and the feature change rate represents the ratio of the feature difference value to the feature value; The marking module selects the index with the largest scaling coefficient among the three analysis indexes as the optimal analysis index of the target feature, maps the optimal analysis index value of the target feature to the radar slice graph, and marks the position corresponding to the maximum value of the optimal analysis index on the radar slice graph; The scoring module calculates the horizontal distance between the marked point and the crack position and the variance of the horizontal distance, takes the weighted sum of the horizontal distance and the variance of the horizontal distance as an evaluation index, calculates the score of the target feature, and traverses all time-domain features and frequency-domain features to obtain the scores of all time-frequency features. The screening module sorts the scores of all time-frequency features from high to low, selects a plurality of time-frequency features with high scores as preliminary screening features, and then statistically analyzes the preliminary screening features of the plurality of target regions, counts the number of occurrences of each preliminary screening feature under all target regions, sorts the preliminary screening features from high to low according to the number of occurrences, and selects a plurality of preliminary screening features with high ranking as the final analysis features.

[0012] The beneficial effects of the present application are as follows: The present application selects features based on the similarity between the crack morphology identified by the features and the actual crack morphology, which can intuitively indicate the sensitivity of different features to cracks, and can greatly reduce the number of features by selecting the features most sensitive to cracks. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Figure 1 A schematic diagram showing a radar slice; Figure 2 A curve graph showing feature values under different channels; Figure 3 A curve graph showing feature difference values under different channels; Figure 4 A curve graph showing feature change rates under different channels; Figure 5 A mapping schematic diagram showing the feature change rate; Figure 6 A schematic diagram of a marked point curve is shown. Figure 7 A morphological similarity analysis result graph is shown. Figure 8 A feature importance ranking result graph is shown. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0015] Please refer to Figures 1-8 The present embodiment provides a ground penetrating radar time-frequency feature selection method based on crack morphology similarity, comprising the following steps: Step S1, obtaining the ground penetrating radar detection result of the road surface, taking the road surface area corresponding to the radar slice graph of the crack annotation box as the target area, and obtaining the radar electromagnetic signal of the target area, wherein the radar electromagnetic signal of the target area includes the radar electromagnetic signal of the crack position and the intact position.

[0016] In the present embodiment, the ground penetrating radar is used in cooperation with the YOLOv8 model, the YOLOv8 model is used for crack identification of the radar slice graph, the crack annotation box is obtained, the position in the crack annotation box is the crack position, and the position outside the crack annotation box is the intact position. This kind of technology belongs to the prior art in the art, for example, the scheme disclosed in patent CN120808181A.

[0017] The process of obtaining the radar electromagnetic signal of the target area specifically comprises the following steps: Step S11, obtaining the name of the radar slice graph where the crack annotation box is located; Step S12, according to the naming rule of the radar slice graph, extracting the trace number range of the radar slice graph through the name determined in step S11; Step S13, according to the trace number range determined in step S12, extracting the radar electromagnetic signal of the target area from the ground penetrating radar detection result.

[0018] The name of the radar slice graph adopts a standardized naming method, which provides position information for subsequent accurate extraction of electromagnetic signals. The naming rule is: detection road section_file number_left boundary trace number of radar slice graph_right boundary trace number of radar slice graph_slice position(depth). For example, Figure 1 as shown, Figure 1The image shows a radar slice (including crack marking boxes) from a road crack detection study. The radar slice is named "1_01_15681_15980_78", where "1" represents the detected road segment; "01" represents the file number; "15681" represents the left boundary road number of the radar slice; "15980" represents the right boundary road number of the radar slice; and "78" represents the slice location (depth).

[0019] Electromagnetic signals at crack locations are accurately extracted by using the range of channel numbers (15681-15980) in the slice image name and a fixed number of channels (300 data points).

[0020] Step S2: Perform time-frequency feature analysis on the radar electromagnetic signal extracted in step S1, and extract multiple time-domain features and frequency-domain features respectively.

[0021] Because crack damage is severely affected by noise and the sample distribution is relatively discrete, in order to select the characteristics reflecting crack damage as comprehensively as possible, a total of 54 time-frequency features were selected in this embodiment, including 30 time-domain features (as shown in Table 1) and 24 frequency-domain features (as shown in Table 2).

[0022] Table 1 Temporal Characteristics Table 2 Frequency Domain Characteristics Step S3: Using any time-frequency feature as the target feature, obtain the feature values ​​of the target area under different channels and calculate the feature difference and feature change rate. Use the ratio of the maximum to minimum value of the three analysis indicators, feature value, feature difference, and feature change rate, under all channels as the scaling factor. Among them, the feature difference represents the feature difference between the crack location and the intact location, and the feature change rate represents the ratio of the feature difference to the feature value.

[0023] The feature values ​​at any location in the target area are directly acquired from the spectrum of time-frequency features. For the calculation of feature difference and feature change rate at intact locations, the feature difference is obtained by subtracting it from the feature value at the crack location and taking the absolute value. Then, the feature difference is divided by the feature value to obtain the feature change rate at the intact location. Similarly, for the calculation of feature difference and feature change rate at crack locations, the feature difference is obtained by subtracting it from the feature value at the intact location and taking the absolute value. Then, the feature difference is divided by the feature value to obtain the feature change rate at the crack location.

[0024] The feature value of the target feature is used to reflect the change of the signal at the crack location itself; the feature difference is used to reflect the difference between the crack location and the intact location; the feature change rate is used to reflect the degree of difference between the crack location and the intact location, thus reflecting the signal fluctuation at the crack location from three dimensions.

[0025] During ground-penetrating radar (GPR) detection, signals from intact locations typically remain stable within a certain range without drastic abrupt changes; however, signals from crack locations exhibit significant fluctuations. The three analytical indicators provided in this invention can reflect the fluctuations at crack locations from different perspectives, but the degree of fluctuation varies among these indicators. To determine which indicator more intuitively reflects the fluctuations at crack locations, this invention defines a scaling factor: A large scaling factor indicates that the analysis index fluctuates violently and can more intuitively reflect the fluctuation of the crack location signal, while a small scaling factor indicates that the analysis index fluctuates gently and cannot intuitively reflect the fluctuation of the crack location signal.

[0026] Taking the mean P0 as the target feature as an example, the characteristic values, characteristic differences, and characteristic rates of change of the crack location are as follows: Figures 2-4 As shown in Table 3, the calculation results of the scaling factor are as follows: Table 3 Scaling Factor Calculation Table from Figures 2-4 It can be seen that the fluctuation trends of eigenvalues, eigend differences, and eigenvalue change rates are basically consistent. As can be seen from Table 3, the eigenvalue scaling factor of the mean P0 is the smallest, while the eigenvalue change rate scaling factor is the largest. This indicates that the eigenvalue change rate can better reflect the fluctuation of the crack location signal and can be better used to distinguish between crack locations and intact locations.

[0027] Step S4: Select the indicator with the largest scaling factor among the three analysis indicators as the optimal analysis indicator of the target feature, map the optimal analysis indicator value of the target feature onto the radar slice map, and mark the position corresponding to the maximum value of the optimal analysis indicator on the radar slice map.

[0028] At the location of the crack, the electromagnetic signal of the ground-penetrating radar will show obvious changes. The signal fluctuation is most intense at the maximum value of the optimal analysis index, indicating that this location is the location of the crack, and it is marked.

[0029] Taking the mean P0 as an example, by analyzing the variation law of the characteristic change rate of the mean P0, the influence of differences in road surface materials on the change of characteristic value can be minimized. The characteristic change rates calculated from the 14 channels of radar electromagnetic signals are mapped onto radar slice images, and the results are as follows: Figure 5 As shown, Figure 5The channels are numbered 1-14 from top to bottom. The labeled result is as follows: Figure 6 As shown.

[0030] Step S5: Calculate the horizontal distance between the marker point and the crack location, as well as the variance of the horizontal distance. Use the weighted sum of the horizontal distance and the variance of the horizontal distance as the evaluation index to calculate the score of the target feature. Iterate through all time-domain features and frequency-domain features to obtain the scores of all time-frequency features.

[0031] Marker points, identified as crack points from target features, form a marker point curve across all channels. By calculating the horizontal distance and variance between the marker point curve and the actual crack location (within the crack marker frame), the similarity between the marker point curve and the crack morphology can be analyzed. This allows for a clear analysis of the variation patterns of various features at the crack, intuitively and clearly demonstrating the sensitivity of different features to cracks. This lays the foundation for determining subsequent analysis features.

[0032] The horizontal distance is used to measure the distance between the marker curve and the crack curve in the morphological space. The smaller the distance, the closer the marker sequence is to the crack morphology. The variance of the horizontal distance is used to describe whether the distribution of marker points is stable and to evaluate whether the marker points are close to the crack.

[0033] Table 4 records the horizontal distances between marker points and crack locations, as well as the variance of these horizontal distances, across 14 channels. The similarity analysis results are as follows: Figure 7 As shown.

[0034] Table 4. Morphological Similarity Results The evaluation indicators are expressed as follows: In the formula, For comprehensive scoring, This is the normalized value of the horizontal distance; This represents the normalized variance of the horizontal distance. , They represent , The weight.

[0035] This implementation method extracts the feature scoring results of a target region, as shown in Table 5: Table 5 Feature Scoring Results Step S6: Sort all time-frequency features from high to low scores, select the top-scoring time-frequency features as initial screening features, and then perform statistical analysis on the initial screening features of multiple target regions. Count the number of times each initial screening feature appears in all target regions, sort them from high to low according to the number of occurrences, and select the top-ranked initial screening features as the final analysis features.

[0036] In this embodiment, the initial screening features are the top 10 time-frequency features with the highest scores for each target region. As shown in Table 5, the initial screening features for this target region are P2, P32, P41, P50, P8, P28, P39, P45, P34, and P49. Following the same method as the target regions in Table 5, the scoring results for 100 target regions are obtained, and the initial screening features for each target region are obtained sequentially.

[0037] The frequency of each initial screening feature was counted across all target regions. Statistical analysis revealed that the top 10 most frequent time-frequency features were P21, P25, P17, P31, P26, P34, P45, P16, P33, and P3. The results are as follows: Figure 8 As shown, through two rounds of filtering, the number of features was reduced from 54 to 10, a reduction of 81%. The final analytical features effectively represent the characteristics of the cracks and can be used for subsequent machine learning. This ensures accurate crack identification while significantly reducing the dimensionality of the data and improving the speed of model processing.

[0038] This embodiment also provides a ground-penetrating radar time-frequency feature selection system based on crack morphology similarity, including: The signal acquisition module is used to acquire the ground-penetrating radar detection results of the road surface. Taking the road surface area corresponding to the radar slice map where the crack marking box is located as the target area, the module acquires the radar electromagnetic signal of the target area. The radar electromagnetic signal of the target area includes the radar electromagnetic signals of the crack location and the intact location. The feature analysis module is used to perform time-frequency feature analysis on the extracted radar electromagnetic signals, extracting multiple time-domain features and frequency-domain features respectively; The analysis index construction module takes any time-frequency feature as the target feature, obtains the feature values ​​of the target area under different channels, and calculates the feature difference and feature change rate. The ratio of the maximum value to the minimum value of the three analysis indices (feature value, feature difference, and feature change rate) under all channels is used as the scaling factor. Among them, the feature difference represents the feature difference between the crack location and the intact location, and the feature change rate represents the ratio of the feature difference to the feature value. The marking module is used to select the indicator with the largest scaling factor among the three analysis indicators as the optimal analysis indicator of the target feature, map the optimal analysis indicator value of the target feature to the radar slice image, and mark the position corresponding to the maximum value of the optimal analysis indicator on the radar slice image. The scoring module calculates the horizontal distance between the marker point and the crack location, as well as the variance of the horizontal distance. It uses the weighted sum of the horizontal distance and the variance of the horizontal distance as the evaluation index to calculate the score of the target feature. It iterates through all time-domain features and frequency-domain features to obtain the scores of all time-frequency features. The filtering module sorts all time-frequency features from highest to lowest score, selects the top-scoring time-frequency features as initial screening features, and then performs statistical analysis on the initial screening features of multiple target regions. It counts the number of times each initial screening feature appears in all target regions and sorts them from highest to lowest according to the number of occurrences. The top-ranked initial screening features are then selected as the final analysis features.

[0039] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other modifications under the guidance of the present invention without departing from the spirit of the present invention, and all of these modifications are within the protection scope of the present invention.

Claims

1. A method for selecting time-frequency features of ground-penetrating radar based on crack morphology similarity, characterized in that, Includes the following steps: Step S1: Obtain the ground-penetrating radar detection results of the road surface. Take the road surface area corresponding to the radar slice map where the crack marking box is located as the target area and obtain the radar electromagnetic signal of the target area. The radar electromagnetic signal of the target area includes the radar electromagnetic signals of the crack location and the intact location. Step S2: Perform time-frequency feature analysis on the radar electromagnetic signal extracted in step S1, and extract multiple time-domain features and frequency-domain features respectively; Step S3: Using any time-frequency feature as the target feature, obtain the feature values ​​of the target area under different channels and calculate the feature difference and feature change rate. Use the ratio of the maximum to minimum value of the three analysis indicators—feature value, feature difference, and feature change rate—under all channels as the scaling factor. Among them, the feature difference represents the feature difference between the crack location and the intact location, and the feature change rate represents the ratio of the feature difference to the feature value. Step S4: Select the indicator with the largest scaling factor among the three analysis indicators as the optimal analysis indicator of the target feature, map the optimal analysis indicator value of the target feature to the radar slice image, and mark the position corresponding to the maximum value of the optimal analysis indicator on the radar slice image. Step S5: Calculate the horizontal distance between the marker point and the crack location, as well as the variance of the horizontal distance. Use the weighted sum of the horizontal distance and the variance of the horizontal distance as the evaluation index to calculate the score of the target feature. Iterate through all time-domain features and frequency-domain features to obtain the scores of all time-frequency features. Step S6: Sort all time-frequency features from high to low scores, select the top-scoring time-frequency features as initial screening features, and then perform statistical analysis on the initial screening features of multiple target regions. Count the number of times each initial screening feature appears in all target regions, sort them from high to low according to the number of occurrences, and select the top-ranked initial screening features as the final analysis features.

2. The ground-penetrating radar time-frequency feature selection method based on crack morphology similarity according to claim 1, characterized in that, The crack annotation boxes are obtained by using the YOLOv8 model to identify cracks in the radar slice image.

3. The ground-penetrating radar time-frequency feature selection method based on crack morphology similarity according to claim 1, characterized in that, The process of acquiring radar electromagnetic signals in the target area includes the following steps: Step S11: Obtain the name of the radar slice containing the crack annotation box; Step S12: According to the naming rules of radar slices, extract the channel number range of the radar slice using the name determined in step S11. Step S13: Based on the channel number range determined in step S12, extract the radar electromagnetic signal of the target area from the ground penetrating radar detection results.

4. The ground-penetrating radar time-frequency feature selection method based on crack morphology similarity according to claim 1, characterized in that, The time-domain features include mean, absolute mean, maximum value, square difference, cubic difference, quartic difference, root mean square, standard deviation, peak-to-peak value, square of mean square, Peak-Po-RMS, skewness, kurtosis, Max-Po-mean, RMS-Po-abs, Max-Po-abs, energy, power, waveform factor, peak factor, impulse factor, zero-crossing rate, average amplitude difference, weighted average, dynamic range, instantaneous mean difference, short-time power change rate, amplitude non-stationarity, autocorrelation, and number of extrema. The frequency-domain features include spectral mean, spectral standard deviation, spectral skewness, spectral kurtosis, weighted frequency average, weighted mean square, weighted variance, fourth-order center distance, peak factor, skewness ratio, third-order kurtosis, fourth-order kurtosis, low-frequency energy ratio, mid-frequency energy ratio, high-frequency energy ratio, spectral centroid, spectral entropy, spectral flatness, frequency variance, bandwidth, harmonic distortion rate, dominant frequency, spectral skewness, and signal-to-noise ratio.

5. The ground-penetrating radar time-frequency feature selection method based on crack morphology similarity according to claim 1, characterized in that, The evaluation indicators are expressed as follows: In the formula: For comprehensive scoring, This is the normalized value of the horizontal distance; This represents the normalized variance of the horizontal distance. , They represent , The weight.

6. The ground-penetrating radar time-frequency feature selection method based on crack morphology similarity according to claim 1, characterized in that, The initial screening features are the top 10 time-frequency features that score highest for each target region; the final analysis features are the top 10 initial screening features that appear most frequently across all target regions.

7. A ground-penetrating radar time-frequency feature selection system based on crack morphology similarity, characterized in that, include: The signal acquisition module is used to acquire the ground-penetrating radar detection results of the road surface. Taking the road surface area corresponding to the radar slice map where the crack marking box is located as the target area, the module acquires the radar electromagnetic signal of the target area. The radar electromagnetic signal of the target area includes the radar electromagnetic signals of the crack location and the intact location. The feature analysis module is used to perform time-frequency feature analysis on the extracted radar electromagnetic signals, extracting multiple time-domain features and frequency-domain features respectively; The analysis index construction module takes any time-frequency feature as the target feature, obtains the feature values ​​of the target area under different channels, and calculates the feature difference and feature change rate. The ratio of the maximum value to the minimum value of the three analysis indices (feature value, feature difference, and feature change rate) under all channels is used as the scaling factor. Among them, the feature difference represents the feature difference between the crack location and the intact location, and the feature change rate represents the ratio of the feature difference to the feature value. The marking module is used to select the indicator with the largest scaling factor among the three analysis indicators as the optimal analysis indicator of the target feature, map the optimal analysis indicator value of the target feature to the radar slice image, and mark the position corresponding to the maximum value of the optimal analysis indicator on the radar slice image. The scoring module calculates the horizontal distance between the marker point and the crack location, as well as the variance of the horizontal distance. It uses the weighted sum of the horizontal distance and the variance of the horizontal distance as the evaluation index to calculate the score of the target feature. It iterates through all time-domain features and frequency-domain features to obtain the scores of all time-frequency features. The filtering module sorts all time-frequency features from highest to lowest score, selects the top-scoring time-frequency features as initial screening features, and then performs statistical analysis on the initial screening features of multiple target regions. It counts the number of times each initial screening feature appears in all target regions and sorts them from highest to lowest according to the number of occurrences. The top-ranked initial screening features are then selected as the final analysis features.

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