A method and system for multi-scale evaluation of road surface anti-skid performance by fusing low-altitude images and anti-skid contribution areas

Abstract

This invention relates to the field of low-altitude economic technology and discloses a multi-scale evaluation method and system for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution areas. The method includes extracting macroscopic texture feature parameter sets for each evaluation unit; extracting microscopic texture feature parameter sets for skid resistance contribution areas from selected measured sample regions; for evaluation units not measured, inputting their macroscopic texture feature parameter sets into a pre-constructed cross-scale feature association model group to obtain estimated values ​​of microscopic texture feature parameters and friction contact area ratio for their skid resistance contribution areas; and integrating macroscopic texture feature parameters, microscopic texture feature parameters, and friction contact area ratio to calculate a comprehensive skid resistance performance evaluation index, thereby determining the skid resistance performance level of each evaluation unit. This invention can accurately deduce the microscopic skid resistance characteristics of the road surface, eliminating the reliance on time-consuming and labor-intensive ground equipment for traditional large-scale road surface microscopic texture detection, and greatly improving the efficiency of road network-level skid resistance evaluation.

Description

Technical Field

[0001] This invention belongs to the field of low-altitude economic technology and relates to a multi-scale evaluation method and system for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution zones. Background Technology

[0002] Road surface anti-skid performance is a core indicator for ensuring vehicle driving safety, as it directly determines the friction between the tire and the road surface. Road surface anti-skid performance is mainly controlled by both macroscopic road surface texture (wavelength 0.5mm~50mm, affecting drainage and road surface hysteresis friction) and microscopic road surface texture (wavelength 0.001mm~0.5mm, affecting adhesive friction).

[0003] Currently, existing evaluation methods mainly focus on single-dimensional skid resistance assessments, such as skid resistance assessments based on tire-road contact prediction, pendulum value (BPN value), and three-dimensional texture (construction depth MPD, root mean square, etc.) (e.g., invention patent CN112818563A discloses a method for evaluating road skid resistance performance based on friction contact surface prediction, and invention patent CN114441436A discloses an analysis method for evaluating skid resistance performance based on road texture). Road surface inspection based on low-altitude UAVs mainly utilizes UAV oblique photography to obtain macroscopic defects (potholes, ruts, etc.), establishes a digital elevation model of the road surface, and performs macroscopic assessments of cracks or smoothness (e.g., invention patent CN112070756A discloses a method for measuring three-dimensional road defects based on UAV oblique photography, invention patent CN117437368A discloses a method, system, terminal, and medium for measuring road smoothness based on UAVs, and invention patent CN109902668A discloses a road surface inspection system and inspection method carried out by UAVs).

[0004] The testing and evaluation of road surface skid resistance performance faces the following technical challenges:

[0005] First, existing large-scale road network detection methods mostly rely on low-altitude drone photography or vehicle-mounted laser scanning. These methods are highly efficient, but limited by equipment resolution and operating height, they can only acquire macroscopic texture parameters at the millimeter (mm) level, and cannot capture the sub-millimeter (sub-mm) microscopic texture features that are crucial to anti-skid performance, resulting in significant deviations in the evaluation results.

[0006] Second, while high-precision road surface detection equipment (such as laser texture scanners) can acquire high-precision microscopic three-dimensional point clouds, their detection efficiency is low. They require point-by-point measurement under closed traffic conditions, which cannot meet the needs of rapid screening of large-area road networks.

[0007] Third, existing texture analysis methods typically perform global statistics on the three-dimensional point cloud of the entire road surface, ignoring the actual physical process of tire-road contact. In reality, only the peak regions of the road surface micro-protrusions (i.e., the "anti-skid contribution areas") truly come into contact with the tire rubber and provide friction. Blindly calculating the global texture will lead to a disconnect between evaluation indicators and actual anti-skid performance.

[0008] Therefore, how to balance the high efficiency of large-scale testing with the high precision of microscopic anti-skid characteristics, and establish a comprehensive evaluation method for anti-skid performance across scales, is a problem that urgently needs to be solved in the field of road engineering. Summary of the Invention

[0009] The purpose of this invention is to provide a multi-scale evaluation method and system for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution areas. This method can accurately calculate the micro-skid resistance characteristics of the road surface, eliminating the reliance on time-consuming and labor-intensive ground equipment for traditional large-scale road surface micro-texture detection, and greatly improving the efficiency of road network-level skid resistance evaluation.

[0010] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution.

[0011] In a first aspect, this invention proposes a multi-scale evaluation method for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution zones, including:

[0012] Step S1: Acquire low-altitude images, and reconstruct the three-dimensional macroscopic point cloud data of the road section to be evaluated based on the acquired low-altitude images; preprocess the three-dimensional macroscopic point cloud data to obtain the macroscopic three-dimensional elevation matrix of the road surface of the road section to be evaluated.

[0013] Step S11: Using a drone equipped with a high-resolution camera, take low-altitude aerial photos of the road section to be evaluated along a preset flight path to obtain a series of low-altitude images with a preset overlap rate; the drone's flight altitude is 10-30m, the forward overlap rate is not less than 80%, the lateral overlap rate is not less than 70%, and the corresponding ground sampling distance is 0.5-3mm / pixel, where pixel represents a pixel.

[0014] Step S12: Perform motion recovery structure processing on the low-altitude image, which includes feature point extraction and matching, sparse point cloud generation, bundle adjustment optimization and dense 3D point cloud reconstruction, to generate 3D macro point cloud data of the road segment to be evaluated, that is, to reconstruct the 3D macro point cloud data of the road segment to be evaluated.

[0015] Step S13: Preprocess the three-dimensional macroscopic point cloud data, including coordinate registration, point cloud denoising, and gridded interpolation, to obtain the macroscopic three-dimensional elevation matrix of the road surface of the road segment to be evaluated. Grid spacing of macroscopic three-dimensional elevation matrix The value range is 0.5–3 mm; among which, Representing the macroscopic scale axis, Representing the macroscopic scale axis.

[0016] Step S2, Extraction of macroscopic texture feature parameters of road surface: Divide the road segment to be evaluated into N evaluation units and extract the macroscopic texture feature parameter set of each evaluation unit.

[0017] Step S2 specifically includes the following sub-steps:

[0018] Step S21: On the macroscopic three-dimensional elevation matrix The road segment to be evaluated is divided into N evaluation units according to the preset evaluation unit size, and the size of each evaluation unit is [size missing]. ,in To evaluate the length of the unit along the driving direction, The horizontal width of the evaluation unit;

[0019] Step S22: For each evaluation unit, extract the macroscopic texture feature parameter set. The macroscopic texture feature parameter set Including average cross-sectional depth Macroscopic arithmetic mean height Macroscopic root mean square height Macro-interface expansion ratio And macroscopic texture volume parameters.

[0020] Among them, average cross-sectional depth Calculated according to ISO 13473-1 standard;

[0021] Macroscopic arithmetic mean height The calculation formula is:

[0022] ;

[0023] in, , The inner edge of the evaluation unit , Number of sampling points in the direction; Indicates the inner edge of the evaluation unit The direction of the first One sampling point, ; Indicates the inner edge of the evaluation unit The direction of the first One sampling point, ; For the first The x-coordinate of each sampling point For the first The ordinate of each sampling point; The average elevation value within the evaluation unit; Indicates along The direction of the first Each sampling point, along The direction of the first The macroscopic three-dimensional elevation matrix of each sampling point;

[0024] Macro root mean square height The calculation formula is:

[0025] ;

[0026] Macro Interface Expansion Ratio The calculation formula is:

[0027] ;

[0028] in, The projected area of ​​the evaluation unit. The actual surface area is, i.e. The corresponding macroscopic three-dimensional elevation matrix area; Data representing the macroscopic three-dimensional elevation matrix, i.e. abbreviation;

[0029] Macroscopic texture volume parameters include peak solid volume. Valley void volume According to the Abbott-Firestone curve method in ISO 25178, the cross-sections at 10% and 80% of the material ratio were used for calculation.

[0030] This yields the macroscopic texture feature parameter set for each evaluation unit. :

[0031] .

[0032] Step S3, Collect microscopic 3D point cloud of road surface and extract skid resistance contribution area: Select the road section to be evaluated. For each measured sample area, the road surface micro-texture is scanned to obtain three-dimensional point cloud data of the road surface micro-texture; the anti-skid contribution area is determined based on the three-dimensional point cloud data of the road surface micro-texture and the pre-constructed friction contact area ratio prediction model; and the micro-texture feature parameter set of the anti-skid contribution area is extracted.

[0033] Step S3 specifically includes the following sub-steps:

[0034] Step S31: Select from the road segments to be evaluated One measured sample area, Each measured sample area is located within one evaluation unit (i.e., no two measured samples are selected within one evaluation unit, and N is greater than or equal to N). One measured sample area represents one measured evaluation unit in this invention. The area of ​​the measured sample area is... ;in This represents the length of the measured sample area, and its value ranges from 50 to 200 mm. This indicates the width of the measured sample area, and its value ranges from 50 to 150 mm.

[0035] Step S32: Use a high-precision laser scanning device to scan the micro-texture of the road surface in each measured sample area to obtain three-dimensional point cloud data of the road surface micro-texture. The scanning resolution is not less than 100μm, and then obtain the micro-three-dimensional elevation matrix. Its grid spacing The value range is 0.05–0.10 mm; among which, Representing the micro-texture scale axis, Representing the micro-texture scale axis.

[0036] Step S33: For each measured sample area, The region is defined as the anti-skid contribution area. The anti-skid contribution area of ​​each measured sample region is obtained through the following steps:

[0037] Step S331: Based on the microscopic three-dimensional elevation matrix of each measured sample area The global three-dimensional texture feature parameter set corresponding to the measured sample area is directly extracted; the global three-dimensional texture feature parameter set includes the arithmetic mean height. Root mean square height skewness , cliff Highlight peak height and the difference in level between the center and the center ;

[0038] Step S332: Using the global three-dimensional texture feature parameter set as input, calculate the friction contact area ratio of the measured sample area using the pre-constructed friction contact area ratio prediction model. The frictional contact area ratio prediction model is a multiple regression model, and its expression is:

[0039] ;

[0040] in, to All are regression coefficients, obtained through pre-calibrated experiments;

[0041] Step S333: Based on the aforementioned frictional contact area ratio In the microscopic three-dimensional elevation matrix Determine the cutting height , making higher than The proportion of the surface area to the total area is equal to ;Will The area is defined as the anti-skid contribution zone;

[0042] Step S34: Extract the micro-texture feature parameter set for the anti-slip contribution area of ​​each measured sample region. The set of micro-texture feature parameters Including the arithmetic mean height of the anti-slip contribution zone Root mean square slope of the anti-slip contribution zone Anti-slip contribution zone interface expansion ratio and peak density of the anti-skid contribution zone .

[0043] Among them, the arithmetic mean height of the anti-slip contribution zone The calculation formula is:

[0044] ;

[0045] in, Elevation data for the anti-slip contribution zone, The average elevation data of the anti-skid contribution zone. , For the anti-skid contribution zone along the inner edge , Number of sampling points in the direction; Indicates the anti-skid contribution zone along the edge The direction of the first One sampling point, ; Indicates the anti-skid contribution zone along the edge The direction of the first One sampling point, ;

[0046] Root mean square slope of anti-skid contribution zone The calculation formula is:

[0047] ;

[0048] Anti-slip contribution area interface expansion ratio The calculation formula is:

[0049] ;

[0050] Peak density in the anti-skid contribution zone Defined as the number of micro-convexity peaks per unit area, its calculation formula is:

[0051] ;

[0052] This yields a set of micro-texture feature parameters. :

[0053] and frictional contact area ratio .

[0054] Step S4: For the measured evaluation units in the road section to be evaluated, integrate macroscopic texture feature parameters, microscopic texture feature parameters, and friction contact area ratio to calculate the comprehensive evaluation index of the anti-skid performance of the measured evaluation units. The specific steps are as follows:

[0055] Step S41: For each measured sample region, simultaneously acquire the macroscopic texture feature parameter set of its evaluation unit. and the set of microtexture feature parameters of the anti-slip contribution area of ​​the measured sample region and frictional contact area ratio ,constitute Grouped sample data;

[0056] Step S42, define the comprehensive evaluation index of multi-scale anti-skid performance. The calculation formula is:

[0057] ;

[0058] in, All are total weight coefficients, and , The value range is 0.2 to 0.4. The value range is 0.3 to 0.5. The value range is 0.15 to 0.30; Indicates macroscopic texture contribution; This indicates the contribution of microtexture to the anti-slip contribution area; This represents the contribution of the frictional contact area ratio.

[0059] Among them, macro texture contribution item The calculation formula is:

[0060] ;

[0061] in, and These are the reference benchmark values ​​for the average cross-sectional depth and the macroscopic interface expansion ratio, respectively, determined based on the 90th percentile value of the parameters of the selected road segment. ;

[0062] For the measured evaluation units, the contribution of microtexture in the anti-slip contribution area The calculation formula is:

[0063] ;

[0064] in, The reference benchmark value representing the root mean square slope of the anti-skid contribution zone; A reference value representing the interface expansion ratio of the anti-slip contribution zone; A reference value representing the peak density of the anti-skid contribution zone; ;

[0065] in, , , , , This represents the sub-weight of the corresponding item.

[0066] For the measured evaluation unit, the contribution of frictional contact area ratio The calculation formula is:

[0067] ;

[0068] in, Frictional contact area ratio The reference benchmark value.

[0069] Step S5: Calculate the comprehensive evaluation index of skid resistance performance for the unmeasured evaluation units (i.e., the evaluation units in the road segment to be evaluated that have not undergone road surface microtexture scanning) in the road segment to be evaluated. The specific steps are as follows:

[0070] Step S51: For the unmeasured evaluation units in the road segment to be evaluated, input the macroscopic texture feature parameter set of the unmeasured evaluation units into a pre-constructed cross-scale feature association model group to obtain the estimated values ​​of each parameter in the microscopic texture feature parameter set of the anti-skid contribution area on the unmeasured evaluation units. and estimated frictional contact area ratio ;

[0071] Among them, the road surface cross-scale feature association model group The construction method is as follows: based on the macroscopic texture feature parameter set Microtexture feature parameter set and frictional contact area ratio Construct a cross-scale feature association model group for road surfaces The specific steps are as follows:

[0072] Step S511, using the macroscopic texture feature parameter set As input, the set of micro-texture feature parameters The estimated values ​​of the four parameters and the estimated frictional contact area ratio are used as outputs to construct a cross-scale feature association model set. ;

[0073] Cross-scale feature association model group The expression is:

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] ;

[0079] in, to All are mapping functions, using a support vector regression model, through the aforementioned The function is obtained by training on paired sample data and is a partial empirical function. This represents the arithmetic mean height estimate of the anti-skid contribution zone. This represents the estimated root mean square slope of the anti-skid contribution zone. This represents the estimated value of the interface expansion ratio of the anti-slip contribution zone. This represents the estimated peak density value in the anti-skid contribution zone. This represents the estimated value of the frictional contact area ratio.

[0080] Step S512: Use cross-validation to test the cross-scale feature association model group. Accuracy verification was performed when the coefficients of determination of each mapping function were... When all values ​​are not lower than 0.75, the cross-scale feature association model group Verification passed; when When the value is below 0.75, increase the number of measured samples. Repeat steps S3 and S511 until the accuracy requirements are met.

[0081] Step S52: Integrate the estimated values ​​of macroscopic texture feature parameters, microscopic texture feature parameter sets, and friction contact area ratio of the unmeasured evaluation units to calculate the comprehensive evaluation index of anti-slip performance of the unmeasured evaluation units. Specifically, it includes the following steps:

[0082] Step S521: Define the comprehensive evaluation index of multi-scale anti-skid performance. The calculation formula is:

[0083] ;

[0084] in, All are total weight coefficients, and , The value range is 0.2 to 0.4. The value range is 0.3 to 0.5. The value range is 0.15 to 0.30; Indicates macroscopic texture contribution; This indicates the contribution of microtexture to the anti-slip contribution area; This represents the contribution of the frictional contact area ratio.

[0085] Among them, macro texture contribution item The calculation formula is:

[0086] ;

[0087] in, and These are the reference benchmark values ​​for the average cross-sectional depth and the macroscopic interface expansion ratio, respectively, determined based on the 90th percentile value of the parameters of the selected road segment. ;

[0088] The contribution of microtexture to the anti-slip contribution area was not measured in the evaluation unit. The calculation formula is:

[0089] ;

[0090] in, The reference benchmark value representing the root mean square slope of the anti-skid contribution zone; A reference value representing the interface expansion ratio of the anti-slip contribution zone; A reference value representing the peak density of the anti-skid contribution zone; ;

[0091] in, , , , , This represents the sub-weight of the corresponding item.

[0092] The contribution of frictional contact area ratio to the evaluation unit not measured in practice. The calculation formula is:

[0093] ;

[0094] in, Frictional contact area ratio The reference benchmark value.

[0095] Step S7, according to The anti-skid performance level of each evaluation unit is determined. The determination method is as follows: when... When, it is judged to have excellent anti-slip performance; when When, it is judged to be qualified in terms of anti-slip performance; when At that time, it was determined that the anti-slip performance was insufficient, and maintenance treatment was required; among them, and The preset grading threshold, and .

[0096] In summary, this invention calculates a comprehensive evaluation index for anti-slip performance by integrating macroscopic texture feature parameters, microscopic texture feature parameters, and frictional contact area ratio for measured evaluation units. Based on this, the anti-skid performance level of each evaluation unit is determined. If the number of laser scanning devices is limited or due to considerations of manpower and material costs, and road surface micro-texture scanning is not performed on every evaluation unit, then there are evaluation units that have not been measured. For evaluation units that have not been measured, the comprehensive anti-skid performance evaluation index is calculated by integrating macro-texture feature parameters, estimated values ​​of micro-texture feature parameters, and estimated values ​​of friction contact area ratio. Based on this, the skid resistance performance level of each evaluation unit is determined, thus realizing the determination of the skid resistance performance level of the entire road section to be evaluated.

[0097] It should be noted that the measured evaluation unit is based on the measured data from micro-texture scanning and low-altitude imagery to obtain macro-texture feature parameters, micro-texture feature parameters, and frictional contact area ratio, which are then used to calculate... For the unmeasured evaluation unit, the macroscopic texture feature parameters are obtained from low-altitude imagery, and then the estimated microscopic texture feature parameters and friction contact area ratio are calculated based on the output of the cross-scale feature association model group. .

[0098] Secondly, this invention proposes a multi-scale evaluation system for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution areas, used to implement the aforementioned multi-scale evaluation method for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution areas, including:

[0099] The low-altitude image acquisition module is configured to reconstruct the three-dimensional macroscopic point cloud data of the road segment to be evaluated based on the low-altitude image, and obtain the macroscopic three-dimensional elevation matrix of the road segment to be evaluated based on the three-dimensional macroscopic point cloud data.

[0100] The macro-texture feature parameter extraction module is configured to divide the road segment to be evaluated into N evaluation units, and extract the macro-texture feature parameter set of each evaluation unit based on the macro-three-dimensional elevation matrix.

[0101] The module for obtaining microtexture feature parameters of the skid resistance contribution zone is configured to select from the road segment to be evaluated. For each measured sample area, the road surface micro-texture is scanned to obtain three-dimensional point cloud data of the road surface micro-texture; the anti-skid contribution area is determined based on the three-dimensional point cloud data of the road surface micro-texture and the pre-constructed friction contact area ratio prediction model; and the micro-texture feature parameter set of the anti-skid contribution area is extracted.

[0102] The module for calculating the comprehensive evaluation index of skid resistance performance of measured evaluation units is configured to calculate the comprehensive evaluation index of skid resistance performance of measured evaluation units in the road section to be evaluated, by integrating macroscopic texture feature parameters, microscopic texture feature parameters, and friction contact area ratio. ;

[0103] The judgment module is configured to evaluate the anti-slip performance of each evaluation unit based on a comprehensive index. The anti-slip performance level of the corresponding evaluation unit is determined.

[0104] In conjunction with the second aspect, it further includes a comprehensive evaluation index calculation module for the anti-skid performance of unmeasured evaluation units. This module is configured to input the macroscopic texture feature parameter set of the unmeasured evaluation units in the road section to be evaluated into a pre-constructed cross-scale feature association model group to obtain the estimated values ​​of each parameter and the estimated value of the friction contact area ratio in the microscopic texture feature parameter set of the anti-skid contribution area on the unmeasured evaluation units.

[0105] By integrating the estimated values ​​of macroscopic texture feature parameters, microscopic texture feature parameter sets, and friction contact area ratio of the unmeasured evaluation units, a comprehensive evaluation index of the anti-slip performance of the unmeasured evaluation units is calculated. .

[0106] Thirdly, the present invention proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-mentioned multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude images and skid resistance contribution areas.

[0107] Fourthly, the present invention provides a computer device comprising:

[0108] Memory, used to store computer programs;

[0109] A processor is used to execute the computer program to implement the steps of the above-described multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude imagery and skid resistance contribution areas.

[0110] Fifthly, the present invention proposes a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude images and skid resistance contribution zones.

[0111] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0112] (1) This invention proposes a two-level detection system of “low-altitude macroscopic screening - ground microscopic precision measurement”. By using a cross-scale mapping model, the microscopic anti-skid characteristics of the road surface can be accurately calculated based on high-efficiency UAV low-altitude data. This eliminates the dependence of traditional large-scale road surface micro-texture detection on time-consuming and labor-intensive ground equipment, and greatly improves the efficiency of road network-level anti-skid evaluation.

[0113] (2) This invention replaces the traditional full-width texture statistics method. It uses the tire contact envelope surface algorithm to directionally separate the "anti-skid contribution area" that truly provides friction, avoiding the interference of the non-contact area of ​​the road surface on the micro-texture parameters, and making the evaluation parameters more realistically reflect the tire-road interaction mechanism.

[0114] (3) This invention proposes a comprehensive evaluation index MSSI based on multi-scale texture feature fusion, which couples the three core friction elements of macro texture, micro texture and contact area ratio, making up for the deviation of existing single-scale or single-parameter evaluation, and the evaluation results are closer to the actual anti-slip decay state. Attached Figure Description

[0115] Figure 1 This is a flowchart illustrating the multi-scale evaluation method for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution zones in Embodiment 1 of the present invention.

[0116] Figure 2 This is a schematic diagram illustrating the composition of the multi-scale anti-skid performance comprehensive evaluation index in Embodiment 1 of the present invention, which integrates low-altitude imagery with the anti-skid contribution zone for multi-scale evaluation of pavement anti-skid performance; wherein... Figure 2 Figure (a) is a schematic diagram of the road pendulum value (BPN value) test; Figure 2 Figure (b) is a schematic diagram of the tire-road contact area, where the blue dot cloud in Figure (b) represents the anti-skid contribution area. Detailed Implementation

[0117] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0118] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0119] Example 1

[0120] Taking the skid resistance evaluation of an asphalt pavement in a certain city as an example, the evaluation method of this invention will be specifically explained. This road section is an AC-13 asphalt mixture pavement, has been in operation for 3 years, and is approximately 2km long. Skid resistance performance testing is required to guide maintenance decisions. For example... Figure 1 As shown in the figure, this embodiment provides a multi-scale evaluation method for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution zones, including the following steps:

[0121] Step S1: Low-altitude image acquisition and reconstruction of a 3D macroscopic model of the road surface.

[0122] A drone (approximately 45 megapixels, 35mm focal length) was used to fly along the centerline of the road segment on a preset flight path. The flight parameters were set as follows: flight altitude 20m, lateral overlap 85%, side overlap 75%, and flight speed 3m / s. Under these flight parameters, the ground sampling distance was approximately 1.5mm / pixel, where pixel represents a unit of area.

[0123] During the flight, the UAV's onboard RTK module recorded the precise exposure position coordinates of each image in real time, thus obtaining the RTK coordinates of each image. This flight collected approximately 5,200 low-altitude images, covering a road surface width of approximately 7.5m (including the wheel track area in the middle of the two-way roadway) and a length of approximately 2,000m.

[0124] The acquired low-altitude images are processed for motion-based structure restoration: First, the SIFT (Scale-Invariant Feature Transform) algorithm is used to extract image feature points and perform cross-image matching to generate a sparse point cloud; then, bundle adjustment is performed, using RTK coordinates as initial exterior orientation elements to jointly optimize camera intrinsic and extrinsic parameters; finally, a dense 3D point cloud of the road segment to be evaluated is generated based on the multi-view stereo matching (MVS) algorithm.

[0125] Preprocessing of dense 3D point clouds: Statistical filtering is used to remove outlier noise points (setting the number of nearest neighbors for each point to 20, and the distance threshold to twice the standard deviation); according to grid spacing... A value of 1.5mm is used to perform regular mesh interpolation on the point cloud to generate a macroscopic three-dimensional elevation matrix of the road surface of the road segment to be evaluated. .in, Representing the macroscopic scale axis, Representing the macroscopic scale axis.

[0126] Step S2: Extraction of macroscopic texture feature parameters of road surface.

[0127] The road sections to be evaluated are arranged according to the evaluation unit size. (in the direction of travel) (Horizontally, corresponding to the width of the wheel track) The entire road segment was divided into approximately 13,300 evaluation units. Among them, Indicates the length of the evaluation unit along the driving direction. This indicates the horizontal width of the evaluation unit.

[0128] For each evaluation unit, from the macroscopic three-dimensional elevation matrix Macroscopic texture feature parameter set is directly extracted from the data. Taking one typical evaluation unit (number AC-0528) as an example, the extraction results are shown in Table 1.

[0129] Table 1 Macro-texture feature parameter set

[0130]

[0131] in, Indicates the average cross-sectional depth. This represents the macroscopic arithmetic mean height. Indicates the macroscopic root mean square height. Indicates the macroscopic interface expansion ratio. Indicates the volume of the peak. This indicates the volume of the void space in the valley.

[0132] Step S3: Acquisition of the microscopic three-dimensional elevation matrix of the road surface and extraction of the anti-skid contribution zone.

[0133] In the road sections to be evaluated, a stratified random sampling method was used to select sections based on road surface type and spatial distribution characteristics of macroscopic texture. One measured sample area, This indicates the number of measured sample areas. The measured sample areas are distributed across the entire length of the road segment, covering road surface conditions with different polishing levels. The dimensions of each measured sample area are 126mm × 76mm (consistent with the contact area of ​​the pendulum instrument's rubber slider, facilitating subsequent calibration of the friction contact area ratio prediction model).

[0134] Each measured sample area was scanned using a laser 3D scanning device to obtain a microscopic 3D elevation matrix. The grid spacing is 0.10mm. Among them, Representing the micro-texture scale axis, Representing the micro-texture scale axis.

[0135] The anti-slip contribution area is extracted for each measured sample area. The specific process is as follows:

[0136] Step S31, from the microscopic three-dimensional elevation matrix Global 3D texture feature parameters are directly extracted. Taking the measured sample SP-15 (located within evaluation unit AC-0528) as an example: , , , , , .in, Indicates the arithmetic mean height; Indicates the root mean square height; Indicates the degree of skewness; Indicates kurtosis; Indicates the height of the prominent peak; This indicates a difference in level at the center.

[0137] Step S32: Input the global three-dimensional texture feature parameters into the friction contact area ratio prediction model. In this embodiment, the regression coefficients of the friction contact area ratio prediction model are obtained through a pre-calibration experiment (the calibration method is as follows: select 30 indoor asphalt mixture rutting slab specimens, coat their surfaces with chalk powder, and repeatedly rub them with a pendulum apparatus until the surface is stable; calculate the friction contact area ratio by the difference between the before and after images, and simultaneously scan to obtain the three-dimensional texture feature parameters, and fit the regression model). The expression of the friction contact area ratio prediction model is:

[0138] ;

[0139] After substituting the specific values, it becomes:

[0140] ;

[0141] in, This represents the frictional contact area ratio.

[0142] Substituting the measured sample SP-15 into the calculation, we get That is, the frictional contact area ratio is approximately 22.3%.

[0143] Step S33, according to In the microscopic three-dimensional elevation matrix Find the corresponding cutting height on the Abbott-Firestone load-bearing capacity curve. , making higher than The area of ​​that area accounts for 22.3% of the total area. Calculations show that... The offset relative to the average elevation is +0.26 mm. The region is extracted as the anti-skid contribution area.

[0144] Step S34: Extract the micro-texture feature parameter set from the anti-slip contribution area of ​​the measured sample SP-15. : , , , .

[0145] in, This represents the arithmetic mean height of the anti-skid contribution zone; This represents the root mean square slope of the anti-slip contribution zone; Indicates the interface expansion ratio of the anti-slip contribution zone; This indicates the peak density of the anti-skid contribution zone.

[0146] Following steps S31 to S34 above, all 30 measured sample areas are scanned to obtain a microscopic three-dimensional elevation matrix, and the anti-slip contribution area is extracted from all 30 measured sample areas to obtain 30 sets of data. Paired sample data.

[0147] At the same time, such as Figure 2 As shown in Figure (a), the BPN value was measured using a pendulum apparatus for each measured sample area, and used as the comprehensive evaluation index for multi-size anti-slip performance. Calibration and verification of the sample SP-15. The measured BPN value of the sample SP-15 was 62.

[0148] Step S4: Construction of cross-scale feature association model set for road surface texture.

[0149] Using the 30 pairs of paired sample data obtained in step S3 as the training set, five mapping functions are constructed respectively:

[0150] by Given the input feature vector, respectively using , , , and To achieve the desired output, a gradient boosting regression algorithm was used to train a cross-scale feature association model for road surface texture. The hyperparameters were set as follows: 200 decision trees, maximum depth of 4 layers, learning rate of 0.05, and minimum number of leaf node samples of 3.

[0151] Cross-validation method was used to evaluate the cross-scale feature association model set. The accuracy, the average coefficient of determination of each mapping function As shown in Table 2.

[0152] Table 2 Cross-scale feature association model group Average coefficient of determination of each mapping function

[0153]

[0154] All mapping functions All are no less than 0.75, cross-scale feature association model group Verification successful.

[0155] Step S5: Calculation of anti-skid characteristics of road surface micro-texture in non-measured sample areas.

[0156] For the remaining approximately 13,260 evaluation units in the unevaluated road sections whose micro-texture scanning was performed, their macro-texture feature parameter sets were individually determined. Input cross-scale feature association model group The estimated values ​​of the microtexture feature parameters of the anti-slip contribution area of ​​the corresponding evaluation unit are obtained. and estimated frictional contact area ratio Taking the evaluation unit AC-2100 as an example: its macroscopic texture feature parameters are as follows: , , , , , Cross-scale feature association model group It is calculated that: , , , , .

[0157] Step S6: Comprehensive evaluation of road surface skid resistance performance using multi-scale fusion.

[0158] In this embodiment, the method for determining the weighting coefficients and reference benchmark values ​​for each parameter is as follows: The reference benchmark values ​​for each parameter are selected as the 90th percentile values ​​of all parameters (i.e., the following 6 parameters) in all evaluation units of this road segment, specifically: , , , , , .in, The reference value representing the average cross-sectional depth. This represents a reference value indicating the macroscopic interface expansion ratio. The reference benchmark value representing the root mean square slope of the anti-skid contribution zone. The reference value representing the interface expansion ratio of the anti-skid contribution zone. The reference value representing the peak density of the anti-skid contribution zone. Expressed as frictional contact area ratio The reference benchmark value.

[0159] The method for determining the weights of sub-items is to calibrate them using constrained least squares regression based on the measured friction coefficients of road sections with known measured friction coefficients, and to optimize the process. , ; , , .

[0160] Multi-scale anti-skid performance comprehensive evaluation index The calculation formula is:

[0161] ;

[0162] in, These represent the total weights of the corresponding items; Indicates macroscopic texture contribution; This indicates the contribution of microtexture to the anti-slip contribution area; This represents the contribution of the frictional contact area ratio.

[0163] The total weighting coefficient is achieved by making A calibration method maximizing the correlation with measured BPN values ​​was employed. Using data from 30 measured sample regions as the calibration set, a nonlinear least squares method was used for optimization. The Pearson correlation coefficient with the BPN value is the largest. The optimization result is... , , .

[0164] Taking evaluation unit AC-0528 (including measured sample SP-15) as an example, the calculation is as follows: :

[0165] ;

[0166] ;

[0167] ;

[0168] ;

[0169] Taking the evaluation unit AC-2100 (non-measured sample area) as an example:

[0170] ;

[0171] ;

[0172] ;

[0173] .

[0174] according to The anti-skid performance level of each evaluation unit is determined. The determination method is as follows: when... When, it is judged to have excellent anti-slip performance; when When, it is judged to be qualified in terms of anti-slip performance; when At that time, it was determined that the anti-slip performance was insufficient, and maintenance treatment was required; among them, and The preset grading threshold, and .

[0175] In this embodiment, the grading threshold is set to... , Therefore, it is determined that the AC-0528 evaluation unit... =0.762>0.70, the anti-skid performance grade is "Excellent"; AC-2100 evaluation unit =0.558, which is between 0.50 and 0.70, and the anti-slip performance level is "qualified".

[0176] All 13,300 evaluation units along the entire road section have been completed. After calculation, the statistical results are as follows: 47.2% of the evaluation units had "excellent" skid resistance performance, 41.5% had "qualified" performance, and 11.3% had "insufficient" performance. The evaluation units with "insufficient" skid resistance performance were mainly concentrated in the downhill sections of long longitudinal slopes and the wheel track areas at intersections, which highly coincided with the key skid-deficient road sections found in actual maintenance inspections.

[0177] To verify The validity of the indicator was calculated in 30 measured sample areas. Pearson correlation coefficient with measured BPN The correlation coefficient was 0.873, and the root mean square error (RMSE) was 3.8 BPN. In comparison, the correlation coefficient between MPD and BPN using only the traditional single indicator was 0.521. The correlation coefficient with BPN is 0.594. These results indicate that MSSI, after fusing multi-scale information, significantly outperforms any single macroscopic texture parameter in characterizing pavement skid resistance.

[0178] Example 2

[0179] Based on the same inventive concept as Embodiment 1, this embodiment introduces a multi-scale evaluation system for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution zones, including:

[0180] The low-altitude image acquisition module is configured to reconstruct the three-dimensional macroscopic point cloud data of the road segment to be evaluated based on the low-altitude image, and obtain the macroscopic three-dimensional elevation matrix of the road segment to be evaluated based on the three-dimensional macroscopic point cloud data.

[0181] The macro-texture feature parameter extraction module is configured to divide the road segment to be evaluated into N evaluation units and extract the macro-texture feature parameter set of each evaluation unit based on the macro-three-dimensional elevation matrix.

[0182] The module for obtaining microtexture feature parameters of the skid resistance contribution zone is configured to select from the road segment to be evaluated. For each measured sample area, the road surface micro-texture is scanned to obtain three-dimensional point cloud data of the road surface micro-texture; the anti-skid contribution area is determined based on the three-dimensional point cloud data of the road surface micro-texture and the pre-constructed friction contact area ratio prediction model; and the micro-texture feature parameter set of the anti-skid contribution area is extracted.

[0183] The module for calculating the comprehensive evaluation index of skid resistance performance of measured evaluation units is configured to calculate the comprehensive evaluation index of skid resistance performance of measured evaluation units in the road section to be evaluated, by integrating macroscopic texture feature parameters, microscopic texture feature parameters, and friction contact area ratio. ;

[0184] The judgment module is configured to evaluate the anti-slip performance of each evaluation unit based on a comprehensive index. The anti-slip performance level of the corresponding evaluation unit is determined.

[0185] In one specific implementation of this embodiment, the multi-scale evaluation system for road skid resistance performance proposed in this embodiment also includes a comprehensive evaluation index calculation module for skid resistance performance of unmeasured evaluation units. This module is configured to input the macroscopic texture feature parameter set of the unmeasured evaluation units in the road section to be evaluated into a pre-constructed cross-scale feature association model group to obtain the estimated values ​​of each parameter and the estimated value of the friction contact area ratio in the microscopic texture feature parameter set of the skid resistance contribution area on the unmeasured evaluation units.

[0186] By integrating the estimated values ​​of macroscopic texture feature parameters, microscopic texture feature parameter sets, and friction contact area ratio of the unmeasured evaluation units, a comprehensive evaluation index of the anti-slip performance of the unmeasured evaluation units is calculated. .

[0187] Example 3

[0188] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the steps of the above-described multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude images and skid resistance contribution zones.

[0189] Example 4

[0190] Based on the same inventive concept as other embodiments, this embodiment introduces a computer device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude images and skid resistance contribution zones.

[0191] Example 5

[0192] Based on the same inventive concept as other embodiments, this embodiment introduces a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude images and skid resistance contribution zones.

[0193] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0195] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0196] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0197] 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, and these modifications are all within the protection scope of the present invention.

Claims

1. A multi-scale evaluation method for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution zones, characterized in that, include: The three-dimensional macro point cloud data of the road segment to be evaluated is reconstructed based on low-altitude imagery, and the macro three-dimensional elevation matrix of the road segment to be evaluated is obtained based on the three-dimensional macro point cloud data. The road segment to be evaluated is divided into N evaluation units, and the macroscopic texture feature parameter set of each evaluation unit is extracted based on the macroscopic three-dimensional elevation matrix. Select from the road sections to be evaluated For each measured sample area, the road surface micro-texture is scanned to obtain three-dimensional point cloud data of the road surface micro-texture; the anti-skid contribution area is determined based on the three-dimensional point cloud data of the road surface micro-texture and the pre-constructed friction contact area ratio prediction model; and the micro-texture feature parameter set of the anti-skid contribution area is extracted. For the measured evaluation units in the road section to be evaluated, the comprehensive evaluation index of the anti-skid performance of the measured evaluation units is calculated by integrating macroscopic texture feature parameters, microscopic texture feature parameters, and friction contact area ratio. ; Based on the comprehensive evaluation index of anti-skid performance of each evaluation unit The anti-slip performance level of the corresponding evaluation unit is determined.

2. The multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude imagery and skid resistance contribution zones as described in claim 1, is characterized in that... It also includes calculating the comprehensive evaluation index of skid resistance performance for unmeasured evaluation units in the road section to be evaluated. The steps are as follows: For unmeasured evaluation units in the road section to be evaluated, the macroscopic texture feature parameter set of the unmeasured evaluation units is input into a pre-constructed cross-scale feature association model group to obtain the estimated values ​​of each parameter and the estimated value of the friction contact area ratio in the microscopic texture feature parameter set of the anti-skid contribution area on the unmeasured evaluation units. By integrating the estimated values ​​of macroscopic texture feature parameters, microscopic texture feature parameter sets, and friction contact area ratio of the unmeasured evaluation units, a comprehensive evaluation index of the anti-slip performance of the unmeasured evaluation units is calculated. .

3. The multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude imagery and skid resistance contribution zones as described in claim 1, characterized in that, The process of reconstructing the three-dimensional macroscopic point cloud data of the road segment to be evaluated based on low-altitude imagery, and obtaining the macroscopic three-dimensional elevation matrix of the road segment to be evaluated based on the three-dimensional macroscopic point cloud data, includes: Using drones, aerial photography is conducted on the road section to be evaluated along a preset route to obtain low-altitude images with a preset overlap rate. The low-altitude image is subjected to motion recovery structure processing, which includes feature point extraction and matching, sparse point cloud generation, bundle adjustment optimization, and dense 3D point cloud reconstruction, to reconstruct the 3D macro point cloud data of the road segment to be evaluated. The three-dimensional macroscopic point cloud data is preprocessed, including coordinate registration, point cloud denoising, and gridded interpolation, to obtain the macroscopic three-dimensional elevation matrix of the road surface of the road segment to be evaluated. ,in, Representing the macroscopic scale axis, Representing the macroscopic scale axis.

4. The multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude imagery and skid resistance contribution zones as described in claim 1, characterized in that, The process involves dividing the road segment to be evaluated into N evaluation units, and based on the macroscopic three-dimensional elevation matrix, extracting the macroscopic texture feature parameter set for each evaluation unit, including: On the macroscopic three-dimensional elevation matrix, the road segment to be evaluated is divided into N evaluation units according to the preset evaluation unit size; For each evaluation unit, a set of macroscopic texture feature parameters is extracted. The macroscopic texture feature parameter set Including average cross-sectional depth Macroscopic arithmetic mean height Macroscopic root mean square height Macro-interface expansion ratio And macroscopic texture volume parameters.

5. The multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude imagery and skid resistance contribution zones as described in claim 1, characterized in that, The selection of road sections to be evaluated For each measured sample area, the road surface micro-texture is scanned to obtain three-dimensional point cloud data of the road surface micro-texture; the anti-skid contribution area is determined based on the three-dimensional point cloud data of the road surface micro-texture and the pre-constructed friction contact area ratio prediction model. The set of microtexture feature parameters of the anti-slip contribution area was extracted, including: Select from the road sections to be evaluated One measured sample area; Laser scanning equipment was used to scan the micro-texture of the road surface in each measured sample area to obtain three-dimensional point cloud data of the road surface micro-texture, and then to obtain the micro-three-dimensional elevation matrix. ,in, Representing the micro-texture scale axis, Representing the micro-texture scale axis; For each measured sample area, The region is defined as the anti-skid contribution area, and the anti-skid contribution area of ​​each measured sample region is obtained; where... Indicates the cutting height; For each measured sample area, extract the set of micro-texture feature parameters for the anti-slip contribution zone. The set of micro-texture feature parameters Including the arithmetic mean height of the anti-slip contribution zone Root mean square slope of the anti-slip contribution zone Anti-slip contribution zone interface expansion ratio and peak density of the anti-skid contribution zone ; Wherein, the will The region is defined as the anti-skid contribution area. The anti-skid contribution area for each measured sample region is obtained, including: Based on the micro three-dimensional elevation matrix of each measured sample area The global three-dimensional texture feature parameter set corresponding to the measured sample area is extracted; the global three-dimensional texture feature parameter set includes the arithmetic mean height. Root mean square height skewness , cliff Highlight peak height and the difference in level between the center and the center ; Using the global three-dimensional texture feature parameter set as input, and employing a pre-constructed frictional contact area ratio prediction model, the frictional contact area ratio of the measured sample area is calculated. The expression for the frictional contact area ratio prediction model is as follows: ； in, to All are regression coefficients; According to the frictional contact area ratio In the microscopic three-dimensional elevation matrix Determine the cutting height , making higher than The proportion of the surface area to the total area is equal to ;Will The area is defined as the anti-skid contribution zone.

6. The multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude imagery and skid resistance contribution zones as described in claim 1, characterized in that, For the measured evaluation units in the road section to be evaluated, the comprehensive evaluation index of the anti-skid performance of the measured evaluation units is calculated by integrating macroscopic texture feature parameters, microscopic texture feature parameters, and friction contact area ratio. ,include: For each measured sample region, the macroscopic texture feature parameter set of its evaluation unit is acquired simultaneously. and the set of microtexture feature parameters of the anti-slip contribution area of ​​the measured sample region and frictional contact area ratio ,constitute Grouped sample data; Define a comprehensive evaluation index for multi-scale anti-skid performance. The calculation formula is: ； in, All are total weight coefficients, and , The value range is 0.2 to 0.

4. The value range is 0.3 to 0.

5. The value range is 0.15 to 0.30; Indicates macroscopic texture contribution; This indicates the contribution of microtexture to the anti-slip contribution area; This represents the contribution of the frictional contact area ratio. Among them, macro texture contribution item The calculation formula is: ； in, and These are the reference benchmark values ​​for the average cross-sectional depth and the macroscopic interface expansion ratio, respectively, determined based on the 90th percentile value of the parameters of the selected road segment. ; For the measured evaluation units, the contribution of microtexture in the anti-slip contribution area The calculation formula is: ； in, The reference benchmark value representing the root mean square slope of the anti-skid contribution zone; A reference value representing the interface expansion ratio of the anti-slip contribution zone; A reference value representing the peak density of the anti-skid contribution zone; ; in, , , , , This indicates the sub-weight of the corresponding item; For the measured evaluation unit, the contribution of frictional contact area ratio The calculation formula is: ； in, Frictional contact area ratio The reference benchmark value.

7. The multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude imagery and skid resistance contribution zones according to claim 2, characterized in that, For the unmeasured evaluation units in the road segment to be evaluated, the macroscopic texture feature parameter set of the unmeasured evaluation units is input into a pre-constructed cross-scale feature association model group to obtain the estimated values ​​of each parameter and the estimated value of the friction contact area ratio in the microscopic texture feature parameter set of the anti-skid contribution area on the unmeasured evaluation unit, including: With the macroscopic texture feature parameter set As input, the set of micro-texture feature parameters The estimated values ​​of the four parameters and the estimated frictional contact area ratio are used as outputs to construct a cross-scale feature association model set. ; Cross-scale feature association model group The expression is: ； ； ； ； ； in, to All are mapping functions; This represents the arithmetic mean height estimate of the anti-skid contribution zone. This represents the estimated root mean square slope of the anti-skid contribution zone. This represents the estimated value of the interface expansion ratio of the anti-slip contribution zone. This represents the estimated peak density value in the anti-skid contribution zone. This represents the estimated value of the frictional contact area ratio.

8. The multi-scale evaluation method for road surface skid resistance performance by fusing low-altitude imagery and skid resistance contribution zones according to claim 7, characterized in that, The comprehensive evaluation index of anti-slip performance of the unmeasured evaluation unit is calculated by integrating the estimated values ​​of macroscopic texture feature parameters, microscopic texture feature parameters, and friction contact area ratio of the unmeasured evaluation unit. ,include: Define a comprehensive evaluation index for multi-scale anti-skid performance. The calculation formula is: ； in, All are total weight coefficients, and , The value range is 0.2 to 0.

4. The value range is 0.3 to 0.

5. The value range is 0.15 to 0.30; Indicates macroscopic texture contribution; This indicates the contribution of microtexture to the anti-slip contribution area; This represents the contribution of the frictional contact area ratio. Among them, macro texture contribution item The calculation formula is: ； in, and These are the reference benchmark values ​​for the average cross-sectional depth and the macroscopic interface expansion ratio, respectively, determined based on the 90th percentile value of the parameters of the selected road segment. ; The contribution of microtexture to the anti-slip contribution area was not measured in the evaluation unit. The calculation formula is: ； in, The reference benchmark value representing the root mean square slope of the anti-skid contribution zone; A reference value representing the interface expansion ratio of the anti-slip contribution zone; A reference value representing the peak density of the anti-skid contribution zone; ; in, , , , , This indicates the sub-weight of the corresponding item; The contribution of frictional contact area ratio to the evaluation unit not measured in practice. The calculation formula is: ； in, Frictional contact area ratio The reference benchmark value.

9. A multi-scale evaluation system for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution zones, characterized in that, The method for multi-scale evaluation of road skid resistance performance by fusing low-altitude imagery and skid resistance contribution zones as described in any of claims 1 to 8 includes: The low-altitude image acquisition module is configured to reconstruct the three-dimensional macroscopic point cloud data of the road segment to be evaluated based on the low-altitude image, and obtain the macroscopic three-dimensional elevation matrix of the road segment to be evaluated based on the three-dimensional macroscopic point cloud data. The macro-texture feature parameter extraction module is configured to divide the road segment to be evaluated into N evaluation units, and extract the macro-texture feature parameter set of each evaluation unit based on the macro-three-dimensional elevation matrix. The module for obtaining microtexture feature parameters of the skid resistance contribution zone is configured to select from the road segment to be evaluated. For each measured sample area, the road surface micro-texture is scanned to obtain three-dimensional point cloud data of the road surface micro-texture; the anti-skid contribution area is determined based on the three-dimensional point cloud data of the road surface micro-texture and the pre-constructed friction contact area ratio prediction model; and the micro-texture feature parameter set of the anti-skid contribution area is extracted. The module for calculating the comprehensive evaluation index of skid resistance performance of measured evaluation units is configured to calculate the comprehensive evaluation index of skid resistance performance of measured evaluation units in the road section to be evaluated, by integrating macroscopic texture feature parameters, microscopic texture feature parameters, and friction contact area ratio. ; The judgment module is configured to evaluate the anti-slip performance of each evaluation unit based on a comprehensive index. The anti-slip performance level of the corresponding evaluation unit is determined.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the multi-scale evaluation method for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution areas as described in any one of claims 1 to 8.

11. A computer device, characterized in that, include: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the multi-scale evaluation method for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution areas as described in any one of claims 1 to 8.

12. A computer program product, comprising a computer program, characterized in that: When the computer program is executed by the processor, it implements the steps of the multi-scale evaluation method for road surface skid resistance performance that integrates low-altitude imagery and skid resistance contribution areas as described in any one of claims 1 to 8.

Images