CN112927204B - Pavement water seepage performance evaluation method based on key water seepage point identification - Google Patents
Pavement water seepage performance evaluation method based on key water seepage point identification Download PDFInfo
- Publication number
- CN112927204B CN112927204B CN202110212832.XA CN202110212832A CN112927204B CN 112927204 B CN112927204 B CN 112927204B CN 202110212832 A CN202110212832 A CN 202110212832A CN 112927204 B CN112927204 B CN 112927204B
- Authority
- CN
- China
- Prior art keywords
- water seepage
- key
- points
- pavement
- point cloud
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 150
- 238000011156 evaluation Methods 0.000 title claims abstract description 12
- 238000000034 method Methods 0.000 claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 24
- 238000013210 evaluation model Methods 0.000 claims abstract description 14
- 239000010426 asphalt Substances 0.000 claims abstract description 12
- 238000001914 filtration Methods 0.000 claims description 16
- 239000002352 surface water Substances 0.000 claims description 14
- 230000035699 permeability Effects 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 9
- 238000003708 edge detection Methods 0.000 claims description 7
- 238000003709 image segmentation Methods 0.000 claims description 7
- 230000035772 mutation Effects 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 4
- 230000005764 inhibitory process Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000003672 processing method Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 4
- 230000007613 environmental effect Effects 0.000 abstract description 3
- 238000012423 maintenance Methods 0.000 abstract description 2
- 230000003449 preventive effect Effects 0.000 abstract description 2
- 230000000007 visual effect Effects 0.000 abstract description 2
- 238000012360 testing method Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 239000011148 porous material Substances 0.000 description 4
- 230000011218 segmentation Effects 0.000 description 4
- 238000002591 computed tomography Methods 0.000 description 3
- 238000000354 decomposition reaction Methods 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 238000010998 test method Methods 0.000 description 3
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 239000000853 adhesive Substances 0.000 description 1
- 230000001070 adhesive effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computer Graphics (AREA)
- Quality & Reliability (AREA)
- Remote Sensing (AREA)
- Image Processing (AREA)
Abstract
The invention relates to a pavement water seepage performance evaluation method based on key water seepage point identification, which comprises the following steps of: s1: measuring and obtaining the average water seepage coefficient of the undamaged pavement and key water seepage points on the pavement; s2: detecting and acquiring three-dimensional point cloud data of a road surface; s3: digital image processing is carried out on the three-dimensional point cloud data, and key water seepage points are identified; s4: calculating parameter information of the identified key water seepage points; s5: and constructing a pavement water seepage performance evaluation model according to the average water seepage coefficient and the parameter information of the key water seepage point, and evaluating the pavement water seepage performance. Compared with the prior art, the method introduces a new information source for the traditional water seepage detection, so as to slow down the risk of water damage to the asphalt pavement, provide a brand new visual angle for preventive maintenance, obtain and collect a plurality of data points without damaging the existing pavement, have high evaluation accuracy and small environmental constraint, can meet daily use, and better evaluate the overall water seepage performance of the pavement.
Description
Technical Field
The invention relates to the field of pavement water seepage performance evaluation, in particular to a pavement water seepage performance evaluation method based on key water seepage point identification.
Background
The water permeability of the asphalt pavement is one of the important standards for measuring the quality of the asphalt pavement, and pore water inside the pavement is the main cause of early water damage of the pavement. The water on the road surface is squeezed into the road surface through the pores by the hydrodynamic pressure to erode the joint of the asphalt and the aggregate until the asphalt film is gradually stripped from the surface of the aggregate, the aggregate begins to loosen, the adhesive force between the aggregates is lost, the structural strength of an asphalt layer is reduced, and the asphalt migration phenomenon occurs, so that the road surface is oiled, stripped and even pits appear.
The traditional pavement water seepage performance evaluation methods mainly comprise three methods, namely a water seepage test method, a CT scanning method and an infrared differential thermal method. The water seepage meter test method obtains the water seepage coefficient by measuring the water seepage volume of the road surface in unit time, however, the method is time-consuming, labor-consuming and low in efficiency, and the test fails easily due to poor sealing performance, and a point-to-surface method is adopted, three test points are randomly selected in a section of road to perform a water seepage test, and the average value of the three test points is taken as the water seepage coefficient index of the whole road section. The CT scanning method estimates the water permeability coefficient by analyzing the relationship between the internal structure of the asphalt mixture and the seepage, however, the CT scanning is destructive detection, requires sampling of the road surface, is time-consuming and labor-consuming, destroys the original road surface structure, and is not suitable for detection of the built road surface. The infrared differential thermal method is influenced by factors such as temperature, sunlight, temperature difference and the like, is greatly restricted by environmental factors, and cannot meet the requirement of daily use.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a pavement water seepage performance evaluation method based on key water seepage point identification.
The purpose of the invention can be realized by the following technical scheme:
a pavement water seepage performance evaluation method based on key water seepage point identification comprises the following steps:
s1: measuring and obtaining the average water seepage coefficient of the undamaged pavement and key water seepage points on the pavement;
s2: detecting and acquiring three-dimensional point cloud data of a road surface;
s3: digital image processing is carried out on the three-dimensional point cloud data, and key water seepage points are identified;
s4: calculating parameter information of the identified key water seepage points;
s5: and constructing a pavement water seepage performance evaluation model according to the average water seepage coefficient and the parameter information of the key water seepage point, and evaluating the pavement water seepage performance.
Preferably, the parameter information of the key water seepage points acquired in step S4 includes the area ratio of the key water seepage points to the total measured road surface and the spatial distribution characteristic coefficient of the key water seepage points.
Preferably, in the step S5, the proportion of the area of the key water seepage point to the total measured road surface and the spatial distribution characteristic coefficient of the key water seepage point are used as weights to perform weighted average, so as to obtain the road surface water seepage performance evaluation model.
Preferably, the evaluation model of the road water permeability is as follows:
C w ＝C w0 +P Q ×C w1
wherein, C w Is the road surface water permeability coefficient, C w0 Average water permeability coefficient, C, for unbroken road surfaces w1 The average water seepage coefficient of the key water seepage points is P, the area of the key water seepage points is in proportion to the total measured pavement, and Q is the spatial distribution characteristic coefficient of the key water seepage points.
Preferably, the method for obtaining the spatial distribution characteristic coefficient of the key water seepage point comprises the following steps: and gridding the three-dimensional point cloud data with the key water seepage point identification result obtained in the step S3, assigning the area detected as the key water seepage point area in the three-dimensional point cloud data to be 1, and assigning the rest to be 0 to obtain gridded data, wherein the two-dimensional entropy of the gridded data is the spatial distribution characteristic coefficient of the key water seepage point.
Preferably, the calculation formula of the spatial distribution characteristic coefficient of the key water seepage point is as follows:
wherein N is the total number of 3 × 3 grids included in the gridding data, and there may be an overlap between different 3 × 3 gridsMoiety, n i Frequency of occurrence of (0, 1) distribution form of the ith 3 × 3 grid, P i The frequency of occurrence of the ith 3 × 3 grid (0, 1) distribution.
Preferably, the key water seepage points in the step S1 include asphalt pavement with aggregate peeling, micro cracks and micro pit grooves.
Preferably, the S2 scans the road surface by using a three-dimensional laser scanning device to obtain three-dimensional point cloud data of the road surface.
Preferably, the step S3 specifically includes:
s31: preprocessing three-dimensional point cloud data;
s32: and processing the three-dimensional point cloud data by adopting a digital image processing method, and identifying key water seepage points.
Preferably, the S31 specifically includes:
s311: performing meshing operation on the three-dimensional point cloud data, meshing the three-dimensional point cloud data at certain intervals, converting spatially dispersed values into regularly distributed grid values, suppressing local noise and making up values of blank grids;
s312: performing interpolation operation on the three-dimensional point cloud data, filling missing values in the three-dimensional point cloud data, filling gaps among the data, increasing point cloud density data, and ensuring the continuity of the three-dimensional point cloud data;
s313: and carrying out filtering and noise reduction operation on the three-dimensional point cloud data, and processing and removing abnormal values of mutation in the data by adopting a two-dimensional filtering method to reduce noise points in the three-dimensional point cloud data.
Preferably, the two-dimensional filtering method in S313 is one of median filtering, mean filtering, and gaussian filtering.
Preferably, the step S32 specifically includes:
s321: decomposing three-dimensional point cloud data by using two-dimensional wavelets, and decomposing and stripping high-frequency parts in the three-dimensional texture to achieve the purpose of removing microscopic texture;
s322: processing the image by using an edge detection algorithm, calculating a gradient amplitude image and an angle image, applying non-maximum inhibition to the gradient amplitude image, detecting and connecting edges by using double-threshold processing and connection analysis, and finding real edges of all key water seepage points;
s323: and applying an image segmentation algorithm to separate the detected abnormal area from the original image through a boundary.
Compared with the prior art, the invention has the following advantages:
(1) the method is characterized by combining the early surface damage of the pavement with the water seepage characteristic, defining the key water seepage point, acquiring the average water seepage coefficient of the undamaged pavement and the key water seepage point, carrying out three-dimensional laser scanning on the pavement, constructing a new water seepage performance evaluation model, introducing a new information source for the traditional water seepage detection, so as to slow down the water damage risk of the asphalt pavement, providing a brand-new visual angle for preventive maintenance, obtaining and collecting a plurality of data points without damaging the existing pavement, having high evaluation accuracy and small environmental constraint, meeting daily use and better evaluating the overall water seepage performance of the pavement;
(2) when the three-dimensional point cloud data of the pavement is processed, preprocessing is performed firstly, and operations such as meshing, interpolation, filtering and noise reduction are performed, so that the noise of the data is effectively reduced, the continuity of the data is improved, the abnormal value of a catastrophe point is reduced, the processing efficiency and the processing accuracy in the subsequent digital image processing are improved, and the identification effect of the method on key seepage points is improved;
(3) the method analyzes, identifies and segments the key seepage point image based on two-dimensional wavelet decomposition, an edge detection algorithm and an image segmentation algorithm to obtain the parameter information of the key seepage point, so that the establishment of a subsequent evaluation model is facilitated;
(4) the method evaluates the pavement water seepage performance based on the parameter information of the key water seepage points, and effectively improves the reliability and the accuracy of a pavement water seepage performance evaluation model by comparing the area of the key water seepage points with the total proportion of the measured pavement and the spatial distribution characteristic coefficient of the key water seepage points.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is three-dimensional point cloud data of a road surface acquired by a laser three-dimensional camera according to the present invention;
FIG. 3 is a schematic diagram of the present invention using two-dimensional wavelets to decompose three-dimensional point cloud data;
FIG. 4 is a schematic diagram of an image processed using an edge detection algorithm according to the present invention;
FIG. 5 is a schematic diagram of an image segmentation algorithm applied in the present invention;
FIG. 6 is a diagram showing the effect of identifying key water seepage points according to the present invention;
FIG. 7 is a diagram showing the effect of identifying key water seepage points according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
A pavement water seepage performance evaluation method based on key water seepage point identification is shown in figure 1 and comprises the following steps:
s1: and measuring and obtaining the average water seepage coefficient of the undamaged pavement and key water seepage points on the pavement.
In an embodiment of the present invention, for a tested pavement, a non-damaged pavement refers to a pavement with uniformly distributed aggregates on the surface, an undamaged pavement and no diseases, the key water seepage points defined in this embodiment refer to a pavement with aggregate peeling, micro cracks, micro pit grooves, etc., a test mark is drawn with chalk, a traditional test method for water seepage coefficient of T0971-2019 asphalt pavement is applied, at least 10 test points are selected from the same pavement, and the average water seepage coefficient of the non-damaged pavement is measured to be C w0 And measuring the average water seepage coefficient of the key water seepage point as C w1 。
S2: and detecting and acquiring three-dimensional point cloud data of the road surface.
And S2, scanning the road surface by using a three-dimensional laser scanning device to acquire three-dimensional texture data of the road surface.
In one embodiment of the invention, three-dimensional texture data of a road surface is obtained by using a three-dimensional laser scanning device, wherein the sampling interval between the x direction (vertical to the driving direction) and the y direction (parallel to the driving direction) is preferably less than 5mm, and high-density point cloud coordinates and texture information with x, y and z coordinate information are derived to obtain the three-dimensional point cloud data of the road surface, and the three-dimensional laser scanning device can be a mobile or fixed device based on a laser triangulation distance measurement principle.
S3: and (4) carrying out digital image processing on the three-dimensional point cloud data, and identifying key water seepage points.
Step S3 specifically includes:
s31: preprocessing three-dimensional point cloud data;
s32: and processing the three-dimensional point cloud data by adopting a digital image processing method, and identifying key water seepage points.
In an embodiment of the present invention, the step of preprocessing the three-dimensional point cloud data by S31 specifically includes:
s311: performing meshing operation on the three-dimensional point cloud data, meshing the three-dimensional point cloud data at certain intervals, converting spatially dispersed values into regularly distributed grid values, suppressing local noise and making up values of blank grids;
s312: performing interpolation operation on the three-dimensional point cloud data, filling missing values in the three-dimensional point cloud data, filling gaps among the data, increasing point cloud density data, and ensuring the continuity of the three-dimensional point cloud data;
s313: and carrying out filtering and noise reduction operation on the three-dimensional point cloud data, and processing and removing abnormal values of mutation in the data by adopting a two-dimensional filtering method to reduce noise points in the three-dimensional point cloud data.
In specific implementation, the interval selection range in S311 is less than 5mm, and high-order interpolation methods such as bilinear interpolation, bicubic interpolation, cubic spline interpolation, lagrange interpolation and the like can be selected in S312, and the abnormal mutation point in S313 is a point in which the sum of the change rates of the point in each direction has a mutation, and is greater than a set threshold, and the threshold is usually set to be 3-5 times of variance.
In this embodiment, the two-dimensional filtering method is one of median filtering, mean filtering, and gaussian filtering.
In an embodiment of the present invention, the step S32 of performing digital image processing on the three-dimensional point cloud data specifically includes:
s321: decomposing three-dimensional point cloud data by using two-dimensional wavelets, and decomposing and stripping high-frequency parts in the three-dimensional texture to achieve the purpose of removing microscopic texture;
s322: processing the image by using an edge detection algorithm, calculating a gradient amplitude image and an angle image, applying non-maximum inhibition to the gradient amplitude image, detecting and connecting edges by using double-threshold processing and connection analysis, and finding real edges of all key water seepage points;
s323: and applying an image segmentation algorithm to separate the detected abnormal area from the original image through a boundary.
When the step S32 is implemented specifically, the three-dimensional point cloud data extracted and preprocessed in the step S31 is used as input, and in the step S321, the two-dimensional wavelet decomposition is applied to decompose and strip the high-frequency part (<1mm) in the three-dimensional texture, so as to achieve the purpose of removing the micro texture; in S322, a canny operator processing image is used for carrying out edge detection processing on the image, a Gaussian filter is used for smoothing the input image, a gradient amplitude image and an angle image are calculated, non-maximum inhibition is applied to the gradient amplitude image, double threshold processing and connection analysis are used for detecting and connecting edges, and the real edges of all key water seepage points are found; in S323, a gradient-based watershed segmentation algorithm is applied to perform image segmentation, resulting in a more stable segmentation result, including connected segmentation boundaries.
S4: and calculating parameter information of the identified key water seepage points. The parameter information of the key water seepage points acquired in the step S4 includes the area ratio of the key water seepage points to the total measured road surface and the spatial distribution characteristic coefficient of the key water seepage points.
S5: and constructing a pavement water seepage performance evaluation model according to the average water seepage coefficient and the parameter information of the key water seepage point, and evaluating the pavement water seepage performance. The road surface water seepage coefficient can be obtained by utilizing the road surface water seepage performance evaluation model, and the road surface water seepage coefficient can be used for evaluating the water seepage performance of the road surface.
In one embodiment of the invention, the proportion of the area of the key water seepage point relative to the total measured road surface and the spatial distribution characteristic coefficient of the key water seepage point are used as weights to carry out weighted average to obtain a road surface water seepage performance evaluation model, and the road surface water seepage performance evaluation model is
C w ＝C w0 +P Q ×C w1
Wherein, C w Is the road surface water permeability coefficient, C w0 Average water permeability coefficient for non-damaged road surface, C w1 The average water seepage coefficient of the key water seepage points is P, the area of the key water seepage points is in proportion to the total measured pavement, and Q is the spatial distribution characteristic coefficient of the key water seepage points.
In the invention, the method for acquiring the spatial distribution characteristic coefficient of the key water seepage point comprises the following steps: and gridding the three-dimensional point cloud data with the key water seepage point identification result obtained in the step S3, assigning the area detected as the key water seepage point area in the three-dimensional point cloud data to be 1, and assigning the rest to be 0 to obtain gridded data, wherein the two-dimensional entropy of the gridded data is the spatial distribution characteristic coefficient of the key water seepage point.
In this embodiment, the mesh size of the three-dimensional point cloud data with the key water seepage point identification result is 5mm × 5mm when the three-dimensional point cloud data is meshed.
Specifically, the calculation formula of the spatial distribution characteristic coefficient of the key water seepage point is as follows:
where N is the total number of 3 × 3 grids included in the gridded data, there may be overlapping portions between different 3 × 3 grids, and N is i Frequency of occurrence of (0, 1) distribution form of the ith 3 × 3 grid, P i The frequency of occurrence of the ith 3 × 3 grid (0, 1) distribution.
The prior art has great volatility in the measurement of the water seepage coefficient of the same road surface. The test result of whether the water permeability coefficient is directly influenced by the existence of the open pores has higher possibility of forming communicated pores at the positions of micro cracks, damages and aggregate peeling-off on the surface, and the surface damages provide space and water for the hydrodynamic pressure, so that the influence of the water damage is higher in probability. With the development of the three-dimensional laser imaging technology, high-precision road surface three-dimensional point cloud data can be obtained through a triangulation distance measuring principle, key water seepage points can be rapidly identified by utilizing road surface three-dimensional texture data, and the distribution of the key water seepage points is analyzed on the basis.
When the method is applied and implemented to a certain road surface, three test road sections are selected, each road section selects ten points of a first type (uniform surface aggregate distribution, undamaged road surface and no diseases) and a second type (key water seepage test points), wherein the measured average water seepage coefficients of the first test road section are respectively C w0 ＝3.22(mL/min)、C w1 The average water permeability coefficient of the second road section is respectively C w0 ＝19.44(mL/min)、C w1 The water permeability coefficient of the two road surface conditions is verified to be greatly different from each other when the road surface conditions are 94.78 (mL/min).
When the method is implemented specifically, a SICK anger3 camera is installed by adopting a slide rail device to collect data, the scanning interval is 0.1mm, and the scanned road texture data is shown in figure 2. And gridding the x and y directions by taking 0.1mm as a unit, and removing abnormal points by two-dimensional median filtering. The preprocessed data is subjected to wavelet decomposition to remove micro-textures, a canny edge detection algorithm is used for processing the image, a gradient-based watershed segmentation algorithm is used for image segmentation, an image recognition algorithm is used for recognizing key seepage points in the image as shown in figures 3-5, and recognition effect graphs of the key seepage points are obtained as shown in figures 6 and 7.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.
Claims (5)
1. A pavement water seepage performance evaluation method based on key water seepage point identification is characterized by comprising the following steps:
s1: measuring and obtaining the average water seepage coefficient of the undamaged pavement and key water seepage points on the pavement;
s2: detecting and acquiring three-dimensional point cloud data of a road surface;
s3: digital image processing is carried out on the three-dimensional point cloud data, and key water seepage points are identified;
s4: calculating parameter information of the identified key water seepage points;
s5: constructing a pavement water seepage performance evaluation model according to the average water seepage coefficient and the parameter information of the key water seepage points, and evaluating the pavement water seepage performance;
the parameter information of the key water seepage points acquired in the step S4 includes the area ratio of the key water seepage points to the total measured road surface and the spatial distribution characteristic coefficient of the key water seepage points;
step S5 is to perform weighted average by using the ratio of the area of the key water seepage points to the total measured road surface and the spatial distribution characteristic coefficient of the key water seepage points as weights to obtain a road surface water seepage performance evaluation model, where the road surface water seepage performance evaluation model is:
C w ＝C w0 +P Q ×C w1
wherein, C w Is the road surface water permeability coefficient, C w0 Average water permeability coefficient for non-damaged road surface, C w1 The average water seepage coefficient of the key water seepage points is defined, P is the area ratio of the key water seepage points to the total measured pavement, and Q is the spatial distribution characteristic coefficient of the key water seepage points;
the method for acquiring the spatial distribution characteristic coefficient of the key water seepage point comprises the following steps: gridding the three-dimensional point cloud data with the key seepage point identification result obtained in the step S3, assigning the area detected as the key seepage point area in the three-dimensional point cloud data as 1, assigning the rest as 0 to obtain gridded data, wherein the two-dimensional entropy of the gridded data is the spatial distribution characteristic coefficient of the key seepage point,
the calculation formula of the space distribution characteristic coefficient Q of the key water seepage point is as follows:
where N is the total number of 3 × 3 grids included in the gridded data, there may be overlapping portions between different 3 × 3 grids, and N is i Frequency of occurrence of (0, 1) distribution form of the ith 3 × 3 grid, P i The frequency of occurrence of the ith 3 × 3 grid (0, 1) distribution.
2. The method for evaluating the road seepage performance based on the identification of the key seepage points in the claim 1, wherein the key seepage points in the step S1 comprise asphalt road surfaces with aggregate peeling, micro cracks and micro pit grooves.
3. The method for evaluating the road surface water seepage performance based on the key water seepage point identification as claimed in claim 1, wherein the step S3 specifically comprises:
s31: preprocessing three-dimensional point cloud data;
s32: and processing the three-dimensional point cloud data by adopting a digital image processing method, and identifying key water seepage points.
4. The method for evaluating the road surface water seepage performance based on the key water seepage point identification as claimed in claim 3, wherein the step S31 specifically comprises the following steps:
s311: performing meshing operation on the three-dimensional point cloud data, meshing the three-dimensional point cloud data at certain intervals, converting spatially dispersed values into regularly distributed grid values, suppressing local noise and making up values of blank grids;
s312: performing interpolation operation on the three-dimensional point cloud data, filling missing values in the three-dimensional point cloud data, filling gaps among the data, increasing point cloud density data, and ensuring the continuity of the three-dimensional point cloud data;
s313: filtering and denoising the three-dimensional point cloud data, and processing and removing abnormal values of mutation in the data by adopting a two-dimensional filtering method to reduce noise points in the three-dimensional point cloud data;
the interval selection range in the step S311 is less than 5 mm.
5. The method for evaluating the road surface water seepage performance based on the key water seepage point identification as claimed in claim 3, wherein the step S32 specifically comprises:
s321: decomposing three-dimensional point cloud data by using two-dimensional wavelets, and decomposing and stripping high-frequency parts in the three-dimensional texture to achieve the purpose of removing microscopic texture;
s322: processing the image by using an edge detection algorithm, calculating a gradient amplitude image and an angle image, applying non-maximum inhibition to the gradient amplitude image, detecting and connecting edges by using double-threshold processing and connection analysis, and finding real edges of all key water seepage points;
s323: and applying an image segmentation algorithm to separate the detected abnormal area from the original image through a boundary.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110212832.XA CN112927204B (en) | 2021-02-25 | 2021-02-25 | Pavement water seepage performance evaluation method based on key water seepage point identification |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110212832.XA CN112927204B (en) | 2021-02-25 | 2021-02-25 | Pavement water seepage performance evaluation method based on key water seepage point identification |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112927204A CN112927204A (en) | 2021-06-08 |
| CN112927204B true CN112927204B (en) | 2022-09-20 |
Family
ID=76171970
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110212832.XA Active CN112927204B (en) | 2021-02-25 | 2021-02-25 | Pavement water seepage performance evaluation method based on key water seepage point identification |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112927204B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105678424A (en) * | 2016-01-30 | 2016-06-15 | 华南理工大学 | Asphalt pavement water damage prediction method |
| CN108760740A (en) * | 2018-05-31 | 2018-11-06 | 同济大学 | A kind of pavement skid resistance condition rapid detection method based on machine vision |
| CN109658398A (en) * | 2018-12-12 | 2019-04-19 | 华中科技大学 | A kind of surface defects of parts identification and appraisal procedure based on three-dimensional measurement point cloud |
| CN110130194A (en) * | 2019-06-25 | 2019-08-16 | 广州肖宁道路工程技术研究事务所有限公司 | A kind of bituminous pavement integrated evaluating method and device |
| CN110738636A (en) * | 2019-09-12 | 2020-01-31 | 北京建筑大学 | Method and system for testing anti-sliding performance of asphalt mixture based on digital image technology |
| CN112070756A (en) * | 2020-09-15 | 2020-12-11 | 河北工业大学 | Three-dimensional road surface disease measuring method based on unmanned aerial vehicle oblique photography |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105956582B (en) * | 2016-06-24 | 2019-07-30 | 深圳市唯特视科技有限公司 | A kind of face identification system based on three-dimensional data |
| CN108010092B (en) * | 2017-12-26 | 2018-11-13 | 哈尔滨工业大学 | A kind of city high density area Solar use potential evaluation method based on low altitude photogrammetry |
| CN108230344B (en) * | 2018-01-24 | 2021-11-02 | 上海勘察设计研究院(集团)有限公司 | Automatic identification method for tunnel water leakage diseases |
| CN109615653B (en) * | 2018-12-19 | 2023-05-05 | 上海大学 | Leakage water area detection and identification method based on deep learning and visual field projection model |
| CN110221277A (en) * | 2019-06-21 | 2019-09-10 | 北京航空航天大学 | A kind of tunnel surface infiltration method for extracting region based on laser radar scanning |
-
2021
- 2021-02-25 CN CN202110212832.XA patent/CN112927204B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105678424A (en) * | 2016-01-30 | 2016-06-15 | 华南理工大学 | Asphalt pavement water damage prediction method |
| CN108760740A (en) * | 2018-05-31 | 2018-11-06 | 同济大学 | A kind of pavement skid resistance condition rapid detection method based on machine vision |
| CN109658398A (en) * | 2018-12-12 | 2019-04-19 | 华中科技大学 | A kind of surface defects of parts identification and appraisal procedure based on three-dimensional measurement point cloud |
| CN110130194A (en) * | 2019-06-25 | 2019-08-16 | 广州肖宁道路工程技术研究事务所有限公司 | A kind of bituminous pavement integrated evaluating method and device |
| CN110738636A (en) * | 2019-09-12 | 2020-01-31 | 北京建筑大学 | Method and system for testing anti-sliding performance of asphalt mixture based on digital image technology |
| CN112070756A (en) * | 2020-09-15 | 2020-12-11 | 河北工业大学 | Three-dimensional road surface disease measuring method based on unmanned aerial vehicle oblique photography |
Non-Patent Citations (2)
| Title |
|---|
| 快速检测沥青路面行驶质量;蒋盛川 等;《中国公路》;20181231(第11期);全文 * |
| 生态化道路***技术发展综述;杜豫川 等;《中国市政工程》;20081031(第5期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112927204A (en) | 2021-06-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | A self organizing map optimization based image recognition and processing model for bridge crack inspection | |
| Ouyang et al. | Pavement cracking measurements using 3D laser-scan images | |
| CN106023185A (en) | Power transmission equipment fault diagnosis method | |
| Schmugge et al. | Crack segmentation by leveraging multiple frames of varying illumination | |
| CN105445160A (en) | Void characteristic of asphalt mixture and extraction method of void characteristic | |
| CN113689445B (en) | High-resolution remote sensing building extraction method combining semantic segmentation and edge detection | |
| CN103674968A (en) | Method and device for evaluating machine vision original-value detection of exterior corrosion appearance characteristics of material | |
| CN115825410B (en) | Method for estimating action noise level of tire road surface based on road surface structure | |
| Li et al. | Pixel-level detection and measurement of concrete crack using faster region-based convolutional neural network and morphological feature extraction | |
| Cao | Research on crack detection of bridge deck based on computer vision | |
| Li et al. | A deep learning-based indoor acceptance system for assessment on flatness and verticality quality of concrete surfaces | |
| CN116563262A (en) | Building crack detection algorithm based on multiple modes | |
| CN115170520A (en) | Metal mesh defect detection method based on structure contrast information lamination | |
| Adu-Gyamfi et al. | Functional evaluation of pavement condition using a complete vision system | |
| Mustaffar et al. | Automated pavement imaging program (APIP) for pavement cracks classification and quantification-a photogrammetric approach | |
| CN112927204B (en) | Pavement water seepage performance evaluation method based on key water seepage point identification | |
| CN107977968A (en) | The building layer detection method excavated based on buildings shadow information | |
| Sidike et al. | Automatic building change detection through adaptive local textural features and sequential background removal | |
| CN115641448A (en) | Method for rapidly identifying height of tree barrier under power line based on unmanned aerial vehicle remote sensing | |
| CN114418965A (en) | Outcrop crack identification method based on laser radar technology | |
| CN110929757B (en) | Method for quickly classifying crack types of asphalt pavement | |
| Marzouk et al. | Quality analysis using three-dimensional modelling and image processing techniques | |
| CN117351013B (en) | Intelligent detection system and method for building damage | |
| CN117152149B (en) | Edge smoothness detection method and system for wafer dicing | |
| CN113592829B (en) | Deep learning sediment particle identification method and device based on segmentation and recombination |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |