Underground condition detection equipment based on three-dimensional ground penetrating radar

Abstract

The application relates to the technical field of underground road detection, and provides underground road condition detection equipment based on a three-dimensional ground penetrating radar. The equipment comprises an underground three-dimensional electromagnetic wave data sensing unit for acquiring underground three-dimensional electromagnetic wave data; a data preprocessing unit for preprocessing the underground three-dimensional electromagnetic wave data to obtain preprocessed data; a three-dimensional imaging unit for splicing the preprocessed data to obtain three-dimensional image data; a suspected target extraction unit for extracting underground suspected targets based on the three-dimensional image data; and an underground condition detection unit for acquiring underground lesion information and road surface layer thickness information based on the underground suspected targets. The application adopts a nondestructive detection mode, can complete underground lesion detection without damaging the road surface, effectively reduces detection cost, shortens the construction period, and minimizes the influence on traffic and citizen travel; and more detailed information of the underground lesion is obtained through three-dimensional detection, so that the missed detection or misjudgment is effectively reduced.

Description

Technical Field

[0001] This application relates to the field of road surface detection technology, specifically to a road surface condition detection device based on three-dimensional ground-penetrating radar. Background Technology

[0002] Subsurface defects, such as roadbed voids, water accumulation in structural layers, hidden cracks, loose areas, and damaged underground pipelines, are the "hidden causes" of road collapse, cracking, and settlement. These defects are characterized by their high degree of concealment and complex spatial morphology; for example, voids may present as irregular cavities, and water accumulation may spread along cracks.

[0003] Traditional roadside inspection methods sometimes rely on "excavation verification," which is not only costly and time-consuming but also disrupts traffic and affects citizens' travel. Other methods rely on two-dimensional detection, such as single-point drilling and two-dimensional radar detection, which are difficult to fully capture detailed information about roadside lesions and are prone to missed detections or misjudgments. Summary of the Invention

[0004] This application provides a road condition detection equipment based on three-dimensional ground-penetrating radar to solve the technical problems of traditional road condition detection, which are high cost, long construction period, traffic disruption and citizen travel disruption, and difficulty in fully capturing detailed information of road lesions, which easily leads to missed detection or misjudgment.

[0005] This application provides a road condition detection device based on three-dimensional ground-penetrating radar, including:

[0006] The under-road three-dimensional electromagnetic wave data sensing unit is used to: acquire under-road three-dimensional electromagnetic wave data;

[0007] The data preprocessing unit is used to: preprocess the three-dimensional electromagnetic wave data under the road to obtain preprocessed data;

[0008] A three-dimensional imaging unit is used to stitch together the preprocessed data to obtain three-dimensional image data.

[0009] The suspected target extraction unit is used to: extract suspected targets under the road based on the three-dimensional image data;

[0010] The under-road condition detection unit is used to: acquire under-road lesion information and pavement layer thickness information based on the suspected under-road targets.

[0011] In one embodiment, the preprocessing of the under-path three-dimensional electromagnetic wave data to obtain preprocessed data includes:

[0012] Based on time-domain filtering and / or frequency-domain filtering, clutter is removed from the three-dimensional electromagnetic wave data under the path to obtain clutter-free data.

[0013] Based on the calibration and correction parameters of multiple measurement lines, the electromagnetic wave data of multiple measurement lines in the clutter-free data are measured for depth correction to obtain depth-corrected data.

[0014] Based on the relative positions between multiple measuring points, the electromagnetic wave data of multiple measuring points in the depth-corrected data are measured and corrected to obtain the position-corrected data.

[0015] The position-corrected data is correlated with spatial coordinates to obtain preprocessed data.

[0016] In one embodiment, the step of stitching together the preprocessed data to obtain three-dimensional image data includes:

[0017] The profile data of multiple survey lines in the preprocessed data are stitched together in sequence to obtain three-dimensional image data.

[0018] In one embodiment, extracting suspected targets based on the three-dimensional image data includes:

[0019] Edge detection is performed on the profile data of each survey line in the three-dimensional image data to obtain targets with strong signal reflection;

[0020] Calculate specific indicators of the strong signal reflection target; the specific indicators include signal reflection difference intensity and continuity parameter;

[0021] Based on the specific indicators, suspected targets are extracted from the road.

[0022] In one embodiment, calculating a specific index of the strong signal reflecting target includes:

[0023] Morphological skeleton extraction is performed on the strong signal reflection target to obtain the strong signal reflection target skeleton;

[0024] For each data point in the strong signal reflection target skeleton, based on a preset direction template, the absolute value of the difference in signal intensity from different directions is calculated to obtain a set of absolute difference values;

[0025] The maximum value in the set of absolute differences is determined as the signal reflection difference intensity of the data point;

[0026] For each connected region in the skeleton of the strong signal reflection target, the length of the connected region is calculated, and the length is determined as the continuity parameter of the connected region.

[0027] In one embodiment, extracting suspected targets off-road based on the specific indicator includes:

[0028] For each connected region in the skeleton of the strong signal reflection target, calculate the average value of the signal reflection difference intensity of the data points in the connected region, and determine the average value as the signal reflection difference intensity of the connected region;

[0029] If the signal reflection difference intensity of the connected region is greater than a first preset reflection difference intensity threshold, and the continuity parameter of the connected region is greater than a preset continuity parameter threshold, then the strong signal reflection target corresponding to the strong signal reflection target skeleton is identified as a suspected off-road target.

[0030] In one embodiment, the subcutaneous lesion information is obtained based on the following method:

[0031] Sub-road suspected targets with a length greater than a preset length threshold are segmented to obtain multiple segments of suspected targets;

[0032] For each of the multiple suspected targets, calculate the average value of the morphological sharpness coefficient of the data points in the suspected target, and determine the average value as the morphological sharpness coefficient of the suspected target.

[0033] If the morphological sharpness coefficient of the sub-suspected target is greater than or equal to the first preset morphological sharpness coefficient threshold, the sub-suspected target is added to the first suspected lesion target set.

[0034] The sub-suspected targets with similar morphological sharpness coefficients in the first set of suspected lesion targets are extended according to the first preset radius to obtain the second set of suspected lesion targets;

[0035] For each connected region in the second set of suspected lesion targets, calculate the length, volume, and projected area of ​​the connected region on the horizontal plane;

[0036] Calculate the average value of the signal reflection difference intensity of all data points within the connected region, and determine the average value as the signal reflection difference intensity of the connected region;

[0037] Based on the length, volume, projected area on the horizontal plane, and signal reflection difference intensity of the connected region, the sub-path lesion information corresponding to the connected region is determined.

[0038] In one embodiment, determining the sub-path lesion information corresponding to the connected region based on the length, volume, projected area on the horizontal plane, and signal reflection difference intensity of the connected region includes:

[0039] Based on the length, volume, projected area on the horizontal plane, and signal reflection difference intensity of the connected region, the suspected defect target type corresponding to the connected region is determined; the suspected defect target type includes suspected crack target, suspected interlayer poor bonding target, suspected void target, and suspected loose target;

[0040] If the length of the connected region corresponding to the suspected crack target is greater than a preset crack length threshold, the under-road lesion corresponding to the connected region is determined to be a reflective crack.

[0041] If the projected area of ​​the connected region corresponding to the suspected poor interlayer bonding target is greater than a preset poor area threshold, and the signal reflection difference intensity of the connected region corresponding to the suspected poor interlayer bonding target is greater than a second preset reflection difference intensity threshold, then the lesion under the road corresponding to the connected region is determined to be poor interlayer bonding.

[0042] If the volume of the connected region corresponding to the suspected vacant target is greater than a preset vacant volume threshold, and the signal reflection difference intensity of the connected region corresponding to the suspected vacant target is greater than a third preset reflection difference intensity threshold, then the lesion under the road corresponding to the connected region is determined to be vacant.

[0043] If the volume of the connected region corresponding to the suspected loose target is greater than a preset loose volume threshold, and the signal reflection difference intensity of the connected region corresponding to the suspected loose target is greater than a fourth preset reflection difference intensity threshold and less than or equal to a third preset reflection difference intensity threshold, then the lesion under the road corresponding to the connected region is determined to be loose.

[0044] In one embodiment, the pavement layer thickness information is obtained based on the following method:

[0045] Calculate the average value of the shape sharpness coefficient of the data points in the suspected off-road target, and determine the average value as the shape sharpness coefficient of the suspected off-road target;

[0046] If the morphological sharpness coefficient of the suspected under-road target is less than the second preset morphological sharpness coefficient threshold, the suspected under-road target is added to the first road surface layer target set.

[0047] For suspected targets under the road with similar sharpness coefficients in the first road surface layered target set, extend them according to the second preset radius to obtain the second road surface layered target set;

[0048] Delete the suspected targets under the road surface whose length is less than the preset layer length threshold in the second road surface layer target set to obtain the third road surface layer target set.

[0049] Based on the third road surface layer target set, and according to the preset statistical interval, the thickness of each structural layer of the road surface is calculated from the top layer downwards, taking into account the road surface structure material.

[0050] In one embodiment, the morphological sharpness coefficient is obtained based on the following method:

[0051] For each data point in the skeleton of the strong signal reflection target, calculate the first slope of the line connecting the data point to a data point within a preset range to its left, and the second slope of the line connecting the data point to a data point within a preset range to its right.

[0052] Calculate the absolute value of the product of the first slope and the second slope to obtain a set of absolute product values;

[0053] The maximum value in the set of absolute values ​​of the product is determined as the morphological sharpness coefficient of the data point.

[0054] In one embodiment, the under-road three-dimensional electromagnetic wave data sensing unit includes a radar host, an antenna system, a mobile system, and a positioning system;

[0055] The radar host is used to control the transmission frequency and pulse interval of electromagnetic waves, and to receive the three-dimensional electromagnetic wave data returned by the antenna system.

[0056] The antenna system is used to transmit high-frequency electromagnetic waves into the ground and receive ground-reflected three-dimensional electromagnetic wave data, and return the ground-reflected three-dimensional electromagnetic wave data to the radar host.

[0057] The mobile system is used to carry the radar host and the antenna system to move along a preset trajectory;

[0058] The positioning system is used to obtain the real-time location of the mobile system.

[0059] This application provides a road surface condition detection equipment based on three-dimensional ground-penetrating radar. The equipment comprises a three-dimensional electromagnetic wave data sensing unit for acquiring road surface three-dimensional electromagnetic wave data, a data preprocessing unit for preprocessing the data to obtain preprocessed data, a three-dimensional imaging unit for stitching the preprocessed data to obtain three-dimensional image data, a suspected target extraction unit for extracting suspected targets based on the three-dimensional image data, and a road surface condition detection unit for acquiring road surface lesion information and pavement layer thickness information based on the suspected targets. This application, through the acquisition, preprocessing, stitching, suspected target extraction, and final lesion identification of road surface three-dimensional electromagnetic wave data, achieves two key benefits. First, the entire process employs a non-destructive testing method, enabling the detection of road surface lesions without damaging the pavement, effectively reducing detection costs, shortening the construction period, and minimizing the impact on traffic and citizens' travel. Second, by obtaining more detailed information about road surface lesions through three-dimensional detection, it effectively reduces missed detections or misjudgments. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a schematic diagram of the road condition detection equipment based on three-dimensional ground-penetrating radar provided in the embodiments of this application. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] It should be noted that in the description of the embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The terms "upper," "lower," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly, for example, they can be fixed connections, detachable connections, or integral connections; they can be mechanical connections or electrical connections; they can be direct connections or indirect connections through an intermediate medium; and they can be internal connections between two elements. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0064] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects have an "or" relationship.

[0065] Figure 1 This is a schematic diagram of the road condition detection equipment based on three-dimensional ground-penetrating radar provided in an embodiment of this application. (Refer to...) Figure 1 This application provides a road condition detection device based on three-dimensional ground-penetrating radar, which may include:

[0066] The under-road three-dimensional electromagnetic wave data sensing unit is used to: acquire under-road three-dimensional electromagnetic wave data;

[0067] The data preprocessing unit is used to: preprocess the three-dimensional electromagnetic wave data to obtain preprocessed data;

[0068] The three-dimensional imaging unit is used to stitch together preprocessed data to obtain three-dimensional image data.

[0069] The suspected target extraction unit is used to: extract suspected targets under the road based on 3D image data;

[0070] The under-road condition detection unit is used to: acquire information on under-road lesions and pavement layer thickness based on suspected under-road targets.

[0071] Among them, the road-under three-dimensional electromagnetic wave data sensing unit uses three-dimensional radar to emit electromagnetic waves into the road and acquire the three-dimensional electromagnetic wave data reflected from the road.

[0072] The under-road condition detection unit can acquire under-road lesion information and pavement layer thickness information as a single functional unit, or it can integrate two functional units, such as an under-road lesion detection unit and a pavement layer thickness detection unit, with the under-road lesion detection unit acquiring under-road lesion information and the pavement layer thickness detection unit acquiring pavement layer thickness information. No limitation is made here.

[0073] This embodiment provides a road condition detection equipment based on three-dimensional ground-penetrating radar. The equipment comprises a three-dimensional electromagnetic wave data sensing unit for acquiring road surface three-dimensional electromagnetic wave data, a data preprocessing unit for preprocessing the data, a three-dimensional imaging unit for stitching the preprocessed data to obtain three-dimensional image data, a suspected target extraction unit for extracting suspected targets based on the three-dimensional image data, and a road condition detection unit for acquiring road surface lesion information and pavement layer thickness information based on the suspected targets. This embodiment, through the acquisition, preprocessing, stitching, suspected target extraction, and final lesion identification of road surface three-dimensional electromagnetic wave data, achieves two key benefits. First, the entire process employs a non-destructive testing method, enabling the detection of road surface lesions without damaging the pavement, effectively reducing detection costs, shortening the construction period, and minimizing the impact on traffic and citizens' travel. Second, by obtaining more detailed information about road surface lesions through three-dimensional detection, it effectively reduces missed detections or misjudgments.

[0074] Furthermore, the current road surface three-dimensional electromagnetic wave data acquired by 3D ground-penetrating radar usually requires manual processing by professionals, which makes it difficult to promote and apply equipment for road surface lesion detection based on 3D ground-penetrating radar. The equipment in this embodiment integrates multiple functional units such as data preprocessing unit, three-dimensional imaging unit, suspected target extraction unit, and road surface condition detection unit to realize intelligent and automatic processing of road surface three-dimensional electromagnetic wave data acquired by 3D ground-penetrating radar, thereby enabling large-scale promotion and application.

[0075] In one embodiment, preprocessing the three-dimensional electromagnetic wave data to obtain preprocessed data may include:

[0076] Based on time-domain filtering and / or frequency-domain filtering, clutter is removed from the three-dimensional electromagnetic wave data to obtain clutter-free data.

[0077] For example, based on wavelet transform filtering, high-frequency or low-frequency components concentrated in the clutter of the three-dimensional electromagnetic wave data can be removed, while retaining the data in the effective frequency band.

[0078] Based on the calibration and correction parameters of multiple measurement lines, the electromagnetic wave data of multiple measurement lines in the data after clutter removal are measured for depth correction to obtain depth-corrected data.

[0079] The calibration parameters can be obtained before the equipment leaves the factory; after depth measurement calibration, the consistency of depth measurement of electromagnetic wave data between multiple measurement lines can be ensured.

[0080] Based on the relative positions between multiple measurement points, the electromagnetic wave data of multiple measurement points in the depth-corrected data are measured and the position is corrected to obtain the position-corrected data.

[0081] The position-corrected data is correlated with spatial coordinates to obtain preprocessed data.

[0082] In this embodiment, clutter interference, depth deviation between survey lines, and position deviation between survey points, which are common in roadbed three-dimensional electromagnetic wave data, are removed and corrected to obtain three-dimensional electromagnetic wave data that can more accurately characterize the roadbed conditions. This data is then correlated with spatial coordinates, and the resulting preprocessed data provides a good data foundation for subsequent data stitching.

[0083] In one embodiment, stitching together the preprocessed data to obtain three-dimensional image data may include:

[0084] The profile data of multiple survey lines in the preprocessed data are stitched together in sequence to obtain three-dimensional image data.

[0085] Since each survey line corresponds to a roadside profile, the preprocessed data actually contains profile data from multiple survey lines. These profile data can be stitched together in sequence to obtain the complete three-dimensional roadside image data.

[0086] This embodiment takes into account the characteristics of multi-line data acquisition by 3D radar, and stitches together the profile data of each line in the preprocessed data in sequence, so as to obtain complete under-road 3D image data.

[0087] In one embodiment, extracting suspected targets from the road surface based on the three-dimensional image data may include:

[0088] Edge detection is performed on the profile data of each survey line in the 3D image data to obtain targets with strong signal reflection;

[0089] Among them, any edge detection method can be used to perform edge detection on the profile data of each survey line, such as template matching, convolutional neural network, etc., which is not limited here; strong signal reflection means that there may be abnormalities such as road lesions or road surface layering in the corresponding sub-road area, so strong signal reflection targets in the three-dimensional image data are extracted for subsequent judgment.

[0090] Calculate specific indicators of targets reflecting strong signals; these indicators include signal reflection difference intensity and continuity parameters.

[0091] This involves calculating multiple indicators related to strong signal reflection targets, subsurface defects, and pavement layering.

[0092] Based on specific indicators, extract suspected targets along the route;

[0093] Based on these specific indicators, targets with strong signal reflection that are most similar to subsurface lesions and pavement layering are extracted as suspected subsurface targets for further identification. Suspected subsurface targets may include suspected pavement layering targets, suspected subsurface lesion targets, etc.

[0094] This embodiment obtains complete strong signal reflection targets by performing edge detection on the profile data of each survey line in the three-dimensional image data. These strong signal reflection targets represent lesions or layered anomalies that may exist in the corresponding under-road area. Then, it calculates various indicators related to these strong signal reflection targets and these anomalies. Based on the indicators, the targets most similar to these anomalies are selected from these strong signal reflection targets as suspected under-road targets. For the sake of caution, these suspected under-road targets need to be further judged to finally determine the anomaly situation in the corresponding under-road area and maximize the accuracy of under-road condition detection.

[0095] In one embodiment, calculating a specific index of a strong signal reflecting target may include:

[0096] Morphological skeleton extraction is performed on the strong signal reflection target to obtain the strong signal reflection target skeleton;

[0097] Specifically, the foreground object of the binary image of the strong signal reflection target can be peeled off layer by layer, and finally a skeleton with a width of one pixel can be extracted. The skeleton retains the basic shape, connectivity and other topological structures of the strong signal reflection target.

[0098] For each data point in the skeleton of the strong signal reflection target, based on the preset direction template, the absolute value of the difference between the signal intensities from different directions of the data point is calculated to obtain a set of absolute difference values. The maximum value in the set of absolute difference values ​​is determined as the signal reflection difference intensity of the data point.

[0099] That is, from the intensity of the signal in different directions of the data point, the two directions with the largest difference are selected, and the absolute value of the difference between the two is determined as the signal reflection difference intensity of the data point.

[0100] For each connected region in the skeleton of a strong signal reflection target, calculate the length of the connected region and determine the length as the continuity parameter of the connected region;

[0101] This embodiment first extracts the skeleton of the strong signal reflection target to reduce the amount of data while preserving the topological structure of the strong signal reflection target, thereby improving the efficiency of subsequent indicator calculations without affecting their accuracy. Furthermore, for each data point in the skeleton, the maximum intensity difference of the signal in different directions is used as the signal reflection difference intensity of that data point, which can improve the sensitivity of subsequent signal reflection difference intensity comparisons. For each connected region in the skeleton, its length is used as the continuity parameter of that connected region, which can more accurately characterize the continuity features of the connected region.

[0102] In one embodiment, extracting suspected targets off-road based on specific indicators may include:

[0103] For each connected region in the skeleton of the strong signal reflection target, calculate the average value of the signal reflection difference intensity of the data points in the connected region, and determine the average value as the signal reflection difference intensity of the connected region;

[0104] If the signal reflection difference intensity of the connected region is greater than the first preset reflection difference intensity threshold, and the continuity parameter of the connected region is greater than the preset continuity parameter threshold, the strong signal reflection target corresponding to the strong signal reflection target skeleton is identified as a suspected target under the road.

[0105] The first preset reflection difference intensity threshold and the preset continuity parameter threshold can be set according to actual needs, and are not limited here.

[0106] In this embodiment, the average signal reflection difference intensity of all data points within the connected region is used as the signal reflection difference intensity of the connected region. This can more accurately characterize the overall signal reflection difference intensity of the connected region. Furthermore, when the signal reflection difference intensity of the connected region is large and its length is long, it can be determined that the connected region is more consistent with the characteristics of road lesions and road surface layering, thereby determining that the strong signal reflection target corresponding to the skeleton is the target most similar to the road lesions and road surface layering, thus achieving accurate extraction of suspected road targets.

[0107] In one embodiment, information on lesions below the lesion can be obtained in the following way:

[0108] Sub-road suspected targets with a length greater than a preset length threshold are segmented to obtain multiple segments of suspected targets;

[0109] Specifically, long suspected targets under the road can be segmented according to an evaluable length threshold to ensure that each segment of suspected targets retains sufficient topological information for subsequent processing; in addition, suspected targets under the road with a length less than or equal to the preset length threshold can be processed as a single segment of suspected targets without segmentation.

[0110] For each sub-suspected target in a multi-segment suspected target, calculate the average value of the morphological sharpness coefficient of the data points in that sub-suspected target, and determine the average value as the morphological sharpness coefficient of that sub-suspected target;

[0111] Among them, the average value of the shape sharpness coefficient of the data points in the sub-suspected target is used as the shape sharpness coefficient of the sub-suspected target, which can more accurately characterize the overall shape sharpness coefficient of the sub-suspected target.

[0112] If the morphological sharpness coefficient of the sub-suspected target is greater than or equal to the first preset morphological sharpness coefficient threshold, the sub-suspected target is added to the first suspected lesion target set.

[0113] That is, to filter out sub-suspected targets with a large morphological sharpness coefficient. The first preset morphological sharpness coefficient threshold can be set according to actual needs and is not limited here.

[0114] The sub-suspected targets with similar morphological sharpness coefficients in the first set of suspected lesions are extended according to the first preset radius to obtain the second set of suspected lesions.

[0115] Sub-suspected targets with large and similar morphological sharpness coefficients are expanded outward to merge within a first preset radius. The first preset radius can be set according to actual needs and is not limited here. The purpose is to integrate sub-suspected targets that may belong to the same suspected lesion target.

[0116] For each connected region in the second set of suspected lesion targets, calculate the length, volume, and projected area on the horizontal plane of the connected region; calculate the average value of the signal reflection difference intensity of all data points in the connected region, and determine the average value as the signal reflection difference intensity of the connected region; based on the length, volume, projected area on the horizontal plane, and signal reflection difference intensity of the connected region, determine the lesion information corresponding to the connected region.

[0117] That is, for each connected region among the integrated suspected lesion targets, its length, volume, projected area on the horizontal plane, and signal reflection difference intensity are calculated, and then the corresponding lesion information is determined based on these indicators.

[0118] In this embodiment, longer suspected targets under the road are first segmented and combined with shorter suspected targets under the road to form multiple sub-suspected targets. Since under-road lesions usually have a significant sharp shape, the sharpness coefficient of each sub-suspected target is calculated. Then, the sub-suspected targets with larger and more similar sharpness coefficients are expanded and connected. The length, volume, projected area on the horizontal plane, and signal reflection difference intensity of each connected region are calculated. Based on these indicators, the under-road lesion information corresponding to the connected region can be accurately determined.

[0119] In one embodiment, determining the sub-path lesion information corresponding to the connected region based on the length, volume, projected area on the horizontal plane, and signal reflection difference intensity of the connected region may include:

[0120] Based on the length, volume, projected area on the horizontal plane, and signal reflection difference intensity of the connected region, the suspected disease target type corresponding to the connected region is determined.

[0121] Suspected defects include suspected cracks, suspected poor interlayer bonding, suspected voids, and suspected loose material.

[0122] If the length of the connected region corresponding to the suspected crack target is greater than the preset crack length threshold, the under-road lesion corresponding to the connected region is determined to be a reflective crack.

[0123] If the projected area of ​​the connected region corresponding to the suspected poor interlayer bonding target is greater than the preset poor area threshold, and the signal reflection difference intensity of the connected region corresponding to the suspected poor interlayer bonding target is greater than the second preset reflection difference intensity threshold, the lesion under the road corresponding to the connected region is determined to be poor interlayer bonding.

[0124] If the volume of the connected region corresponding to the suspected detached target is greater than the preset detached volume threshold, and the signal reflection difference intensity of the connected region corresponding to the suspected detached target is greater than the third preset reflection difference intensity threshold, the lesion under the road corresponding to the connected region is determined to be detached.

[0125] If the volume of the connected region corresponding to the suspected loose target is greater than the preset loose volume threshold, and the signal reflection difference intensity of the connected region corresponding to the suspected loose target is greater than the fourth preset reflection difference intensity threshold and less than or equal to the third preset reflection difference intensity threshold, the lesion under the road corresponding to the connected region is determined to be loose.

[0126] Among them, the preset crack length threshold, preset defect area threshold, second preset reflection difference intensity threshold, preset void volume threshold, third preset reflection difference intensity threshold, preset loose volume threshold, and fourth preset reflection difference intensity threshold can all be set according to actual needs, and are not limited here; however, the second preset reflection difference intensity threshold is greater than the aforementioned first preset reflection difference intensity threshold, the third preset reflection difference threshold is greater than the second preset reflection difference threshold, and the fourth preset reflection difference threshold is less than the third preset reflection difference threshold.

[0127] This embodiment first determines the suspected disease target type corresponding to the connected region based on the length, volume, projected area on the horizontal plane, and signal reflection difference intensity of the connected region. Then, for different suspected disease target types, it further determines the under-road lesion information corresponding to the connected region based on the specific indicators of the corresponding connected region. This enables a differentiated comparison of the characteristics of different suspected disease target types in terms of length, volume, projected area on the horizontal plane, and signal reflection difference intensity, thereby accurately obtaining the under-road lesion information corresponding to the connected region where different suspected disease target types are located.

[0128] In one embodiment, pavement layer thickness information can be obtained in the following way:

[0129] Calculate the average value of the shape sharpness coefficient of the data points among the suspected targets under the road, and determine the average value as the shape sharpness coefficient of the suspected target under the road;

[0130] Among them, the average value of the shape sharpness coefficient of the data points in the suspected targets under the road is used as the shape sharpness coefficient of the suspected targets under the road, which can more accurately characterize the overall shape sharpness coefficient of the suspected targets under the road.

[0131] If the sharpness coefficient of the suspected target under the road is less than the second preset sharpness coefficient threshold, the suspected target under the road is added to the first road surface layer target set;

[0132] This means filtering out suspected targets on the road with a smaller shape sharpness coefficient. The second preset shape sharpness coefficient threshold can be set according to actual needs and is not limited here; however, the second preset shape sharpness coefficient threshold is less than the first preset shape sharpness coefficient threshold.

[0133] For suspected targets under the road with similar sharpness coefficients in the first road surface layer target set, extend them according to the second preset radius to obtain the second road surface layer target set;

[0134] The approach involves expanding outwards the suspected targets under the road with relatively small and similar morphological sharpness coefficients to merge them within a second preset radius. This second preset radius can be set according to actual needs and is not limited here. The purpose is to integrate suspected targets under the road that may belong to the same road surface layer.

[0135] Delete the suspected targets under the road surface whose length is less than the preset layer length threshold in the second road surface layer target set to obtain the third road surface layer target set.

[0136] That is, to select the road surface layer targets with the longer length from the integrated road surface layer targets;

[0137] Based on the third pavement layer target set, and according to the preset statistical interval, the thickness of each pavement structural layer is calculated from the top layer downwards, taking into account the pavement structure material.

[0138] That is, according to the preset statistical interval, the road layer targets in the third road layer target set are obtained from the top layer of the road surface downwards. Combined with the relative permittivity of the road structure layer where each road layer target is located, the depth-corrected data corresponding to each road layer target is corrected again, and then the thickness of the road structure layer is calculated.

[0139] In this embodiment, since the road surface layers usually have insignificant sharp shapes, the sharpness coefficient of each suspected target under the road can be calculated first. Then, the suspected targets under the road with smaller and more similar sharpness coefficients can be expanded and connected. Then, the relatively larger road surface layer targets can be selected. Combined with the relative permittivity of the road surface structural layer in which they are located, the thickness of each structural layer of the road surface can be accurately calculated.

[0140] In one embodiment, the morphological sharpness coefficient can be obtained based on the following method:

[0141] For each data point in the skeleton of the strong signal reflection target, calculate the first slope of the line connecting the data point to the data point within a preset range to its left, and the second slope of the line connecting the data point to the data point within a preset range to its right.

[0142] Calculate the absolute value of the product of the first slope and the second slope to obtain the set of absolute values ​​of the product;

[0143] The maximum value in the set of absolute values ​​of the product is determined as the morphological sharpness coefficient of the data point.

[0144] The larger the absolute value of the first slope or the second slope, the farther the horizontal plane where the data point is located is from the horizontal plane where the data point to its left or right is located. In this case, the data point is likely to be a data inflection point, that is, a sharp point in shape. When the absolute value of the product of the first slope and the second slope is the largest, it means that the absolute values ​​of the first slope and the second slope are likely to have reached their maximum values. At this time, the data point is the sharpest. The absolute value of the product of the first slope and the second slope at this time is used as the shape sharpness coefficient of the data point to improve the sensitivity of subsequent shape sharpness coefficient comparisons.

[0145] This embodiment characterizes the shape of the data distribution on both sides of a data point based on the slope of the line connecting the data point to its left and right sides. Then, the absolute value of the product of the two slopes is used to measure the sharpness of the data point, and the maximum absolute value of the product is used as the shape sharpness coefficient of the data point, so as to achieve the maximum quantification of the sharpness of each data point and improve the sensitivity of subsequent comparisons.

[0146] Reference Figure 1 In one embodiment, the under-road three-dimensional electromagnetic wave data sensing unit may include a radar host, an antenna system, a mobile system, and a positioning system;

[0147] The radar host is used to control the transmission frequency and pulse interval of electromagnetic waves, and to receive the three-dimensional electromagnetic wave data returned by the antenna system. It can also record the reflected signal strength, propagation time, and location information of the three-dimensional electromagnetic wave data in real time.

[0148] The antenna system is used to transmit high-frequency electromagnetic waves into the ground and receive the ground three-dimensional electromagnetic wave data reflected from the ground, and return the ground three-dimensional electromagnetic wave data to the radar host. The antenna system can be a multi-antenna array, such as multiple antenna groups arranged in a linear fashion, to acquire ground three-dimensional electromagnetic wave data of multiple survey lines.

[0149] A mobile system used to carry the radar main unit and antenna system along a preset trajectory;

[0150] A positioning system is used to obtain the real-time location of a mobile system; specifically, it can obtain the real-time location of a mobile system through one or more of GPS / BeiDou positioning, encoders, and inertial navigation systems.

[0151] In this embodiment, the under-road three-dimensional electromagnetic wave data sensing unit, through the cooperation of the radar host, antenna system, mobile system and positioning system, can acquire rich and accurate under-road three-dimensional electromagnetic wave data in real time during the under-road condition detection process, providing a good data foundation for subsequent data processing.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A road condition detection device based on three-dimensional ground-penetrating radar, characterized in that, include: The under-road three-dimensional electromagnetic wave data sensing unit is used to: acquire under-road three-dimensional electromagnetic wave data; The data preprocessing unit is used to: preprocess the three-dimensional electromagnetic wave data under the road to obtain preprocessed data; A three-dimensional imaging unit is used to stitch together the preprocessed data to obtain three-dimensional image data. The suspected target extraction unit is used to: extract suspected targets under the road based on the three-dimensional image data, including: Edge detection is performed on the profile data of each survey line in the three-dimensional image data to obtain targets with strong signal reflection; Calculate specific indicators of the strong signal reflection target, including: The specific indicators include signal reflection difference intensity and continuity parameters; Morphological skeleton extraction is performed on the strong signal reflection target to obtain the strong signal reflection target skeleton; For each data point in the strong signal reflection target skeleton, based on a preset direction template, the absolute value of the difference in signal intensity from different directions is calculated to obtain a set of absolute difference values; The maximum value in the set of absolute differences is determined as the signal reflection difference intensity of the data point; For each connected region in the skeleton of the strong signal reflection target, the length of the connected region is calculated, and the length is determined as the continuity parameter of the connected region; Based on the specific indicators, extract suspected targets along the route; The under-road condition detection unit is used to: acquire under-road lesion information and pavement layer thickness information based on the suspected under-road targets.

2. The road condition detection equipment based on three-dimensional ground-penetrating radar according to claim 1, characterized in that, The preprocessing of the three-dimensional electromagnetic wave data under the road to obtain preprocessed data includes: Based on time-domain filtering and / or frequency-domain filtering, clutter is removed from the three-dimensional electromagnetic wave data under the path to obtain clutter-free data. Based on the calibration and correction parameters of multiple measurement lines, the electromagnetic wave data of multiple measurement lines in the clutter-free data are measured for depth correction to obtain depth-corrected data. Based on the relative positions between multiple measuring points, the electromagnetic wave data of multiple measuring points in the depth-corrected data are measured and corrected to obtain the position-corrected data. The position-corrected data is correlated with spatial coordinates to obtain preprocessed data.

3. The road condition detection equipment based on three-dimensional ground-penetrating radar according to claim 1, characterized in that, The process of stitching together the preprocessed data to obtain three-dimensional image data includes: The profile data of multiple survey lines in the preprocessed data are stitched together in sequence to obtain three-dimensional image data.

4. The road condition detection equipment based on three-dimensional ground-penetrating radar according to claim 1, characterized in that, The extraction of suspected targets under the road based on the specific indicators includes: For each connected region in the skeleton of the strong signal reflection target, calculate the average value of the signal reflection difference intensity of the data points in the connected region, and determine the average value as the signal reflection difference intensity of the connected region; If the signal reflection difference intensity of the connected region is greater than a first preset reflection difference intensity threshold, and the continuity parameter of the connected region is greater than a preset continuity parameter threshold, then the strong signal reflection target corresponding to the strong signal reflection target skeleton is identified as a suspected off-road target.

5. The road condition detection equipment based on three-dimensional ground-penetrating radar according to claim 1, characterized in that, The information on lesions below the lesion site was obtained based on the following methods: Sub-road suspected targets with a length greater than a preset length threshold are segmented to obtain multiple segments of suspected targets; For each of the multiple suspected targets, calculate the average value of the morphological sharpness coefficient of the data points in the suspected target, and determine the average value as the morphological sharpness coefficient of the suspected target. If the morphological sharpness coefficient of the sub-suspected target is greater than or equal to the first preset morphological sharpness coefficient threshold, the sub-suspected target is added to the first suspected lesion target set. The sub-suspected targets with similar morphological sharpness coefficients in the first set of suspected lesion targets are extended according to the first preset radius to obtain the second set of suspected lesion targets; For each connected region in the second set of suspected lesion targets, calculate the length, volume, and projected area of ​​the connected region on the horizontal plane; Calculate the average value of the signal reflection difference intensity of all data points within the connected region, and determine the average value as the signal reflection difference intensity of the connected region; Based on the length, volume, projected area on the horizontal plane, and signal reflection difference intensity of the connected region, the sub-path lesion information corresponding to the connected region is determined.

6. The road condition detection equipment based on three-dimensional ground-penetrating radar according to claim 5, characterized in that, The determination of the sub-path lesion information corresponding to the connected region based on the length, volume, projected area on the horizontal plane, and signal reflection difference intensity of the connected region includes: Based on the length, volume, projected area on the horizontal plane, and signal reflection difference intensity of the connected region, the suspected defect target type corresponding to the connected region is determined; the suspected defect target type includes suspected crack target, suspected interlayer poor bonding target, suspected void target, and suspected loose target; If the length of the connected region corresponding to the suspected crack target is greater than a preset crack length threshold, the under-road lesion corresponding to the connected region is determined to be a reflective crack. If the projected area of ​​the connected region corresponding to the suspected poor interlayer bonding target is greater than a preset poor area threshold, and the signal reflection difference intensity of the connected region corresponding to the suspected poor interlayer bonding target is greater than a second preset reflection difference intensity threshold, then the lesion under the road corresponding to the connected region is determined to be poor interlayer bonding. If the volume of the connected region corresponding to the suspected vacant target is greater than a preset vacant volume threshold, and the signal reflection difference intensity of the connected region corresponding to the suspected vacant target is greater than a third preset reflection difference intensity threshold, then the lesion under the road corresponding to the connected region is determined to be vacant. If the volume of the connected region corresponding to the suspected loose target is greater than a preset loose volume threshold, and the signal reflection difference intensity of the connected region corresponding to the suspected loose target is greater than a fourth preset reflection difference intensity threshold and less than or equal to a third preset reflection difference intensity threshold, then the lesion under the road corresponding to the connected region is determined to be loose.

7. The road condition detection equipment based on three-dimensional ground-penetrating radar according to claim 1, characterized in that, The pavement layer thickness information was obtained in the following way: Calculate the average value of the shape sharpness coefficient of the data points in the suspected off-road target, and determine the average value as the shape sharpness coefficient of the suspected off-road target; If the morphological sharpness coefficient of the suspected under-road target is less than the second preset morphological sharpness coefficient threshold, the suspected under-road target is added to the first road surface layer target set. For suspected targets under the road with similar sharpness coefficients in the first road surface layered target set, extend them according to the second preset radius to obtain the second road surface layered target set; Delete the suspected targets under the road surface whose length is less than the preset layer length threshold in the second road surface layer target set to obtain the third road surface layer target set. Based on the third road surface layer target set, and according to the preset statistical interval, the thickness of each structural layer of the road surface is calculated from the top layer downwards, taking into account the road surface structure material.

8. The road condition detection equipment based on three-dimensional ground-penetrating radar according to claim 5 or 7, characterized in that, The morphological sharpness coefficient is obtained based on the following method: For each data point in the skeleton of the strong signal reflection target, calculate the first slope of the line connecting the data point to a data point within a preset range to its left, and the second slope of the line connecting the data point to a data point within a preset range to its right. Calculate the absolute value of the product of the first slope and the second slope to obtain a set of absolute product values; The maximum value in the set of absolute values ​​of the product is determined as the morphological sharpness coefficient of the data point.

9. The road condition detection equipment based on three-dimensional ground-penetrating radar according to claim 1, characterized in that, The under-road three-dimensional electromagnetic wave data sensing unit includes a radar host, an antenna system, a mobile system, and a positioning system; The radar host is used to control the transmission frequency and pulse interval of electromagnetic waves, and to receive the three-dimensional electromagnetic wave data returned by the antenna system. The antenna system is used to transmit high-frequency electromagnetic waves into the ground and receive ground-reflected three-dimensional electromagnetic wave data, and return the ground-reflected three-dimensional electromagnetic wave data to the radar host. The mobile system is used to carry the radar host and the antenna system to move along a preset trajectory; The positioning system is used to obtain the real-time location of the mobile system.

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