CN115164762A - Pavement rut fine measurement method based on structured light - Google Patents
Pavement rut fine measurement method based on structured light Download PDFInfo
- Publication number
- CN115164762A CN115164762A CN202210785268.5A CN202210785268A CN115164762A CN 115164762 A CN115164762 A CN 115164762A CN 202210785268 A CN202210785268 A CN 202210785268A CN 115164762 A CN115164762 A CN 115164762A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- rut
- cloud data
- area
- structured light
- 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.)
- Pending
Links
- 238000000691 measurement method Methods 0.000 title claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 36
- 238000001514 detection method Methods 0.000 claims abstract description 20
- 238000005259 measurement Methods 0.000 claims abstract description 15
- 238000013507 mapping Methods 0.000 claims abstract description 14
- 238000012545 processing Methods 0.000 claims abstract description 13
- 238000011156 evaluation Methods 0.000 claims abstract description 7
- 238000007781 pre-processing Methods 0.000 claims description 5
- 238000012706 support-vector machine Methods 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000013527 convolutional neural network Methods 0.000 claims description 3
- 239000000126 substance Substances 0.000 claims 1
- 238000012423 maintenance Methods 0.000 abstract description 7
- 238000007500 overflow downdraw method Methods 0.000 abstract description 3
- 238000005516 engineering process Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 125000004122 cyclic group Chemical group 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 229910002056 binary alloy Inorganic materials 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/22—Measuring arrangements characterised by the use of optical techniques for measuring depth
-
- E—FIXED CONSTRUCTIONS
- E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
- E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
- E01C23/00—Auxiliary devices or arrangements for constructing, repairing, reconditioning, or taking-up road or like surfaces
- E01C23/01—Devices or auxiliary means for setting-out or checking the configuration of new surfacing, e.g. templates, screed or reference line supports; Applications of apparatus for measuring, indicating, or recording the surface configuration of existing surfacing, e.g. profilographs
-
- G06T5/70—
-
- 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
- G06T7/0004—Industrial image inspection
-
- 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
Abstract
The invention provides a road rut fine measurement method based on structured light, which comprises the steps of denoising, gradient removing and two-dimensional rasterization processing of collected road point cloud data, mapping various features of the rasterized point cloud data into a gray image, and finally realizing identification and fine measurement of ruts based on an image identification and multi-feature fusion method. Because the point cloud data acquisition is carried out by adopting the vehicle-mounted structured light acquisition equipment, the measurement of the degree of the rut on the road surface under the dynamic traffic flow speed can be supported, and the quick and fine rut detection and fine evaluation can be realized. Meanwhile, a more detailed road damage condition is provided for a road maintenance department, and a basis is provided for maintenance fund distribution and measure selection. And because the point cloud data are processed by adopting two-dimensional rasterization, the calculated amount of point cloud data processing and the dependence on manual or high-precision instruments are greatly reduced, and the method has important significance on large-scale pavement performance high-frequency digital detection.
Description
Technical Field
The invention belongs to the technical field of pavement quality detection and automatic information acquisition, and particularly relates to a pavement rut fine measurement method based on structured light.
Background
Ruts are permanent deformations of the road surface caused by repeated running and rolling of the vehicle tires on the track line of the roadway. In recent years, with the rapid increase of traffic volume and the aggravation of heavy load and overload, ruts become one of the main factors influencing the service performance of roads, so that the service performance of the roads is directly influenced, the safety and the comfort of the roads are directly improved, the structural integrity and the stability of the roads are seriously damaged, the service life of the roads is shortened, and the maintenance cost is increased.
The rut detection technology can be mainly divided into two types, namely manual detection and automatic detection. The traditional rut measuring method based on manual contact, such as an AASHTO rut gauge, a surface height gauge, a hand-push type section instrument and the like, has low measuring efficiency, large influence on measuring precision and accuracy by personnel, random errors and is not suitable for large-scale rut detection. With the rapid development of laser technology, the three-dimensional laser technology has become mature gradually and is beginning to be applied to the field of pavement detection, and the existing automatic detection equipment is laser equipment, including point laser rutting instruments and line laser rutting instruments. The point laser adopts a triangular distance measurement principle, the receiving direction is not variable, one point is processed each time, and the measuring speed is slow; the line laser projects plane slit light through a projection source, and a section depth is obtained by projecting a structured light stripe each time, but the vehicle-mounted line laser is easily affected by vehicle jolt and self vibration, and the precision is greatly reduced. Therefore, compared with the disadvantages of low efficiency and large error of the existing rut measuring equipment, the advanced surface structured light 3D snapshot technology provides possibility for large-scale rut detection.
The surface structure optical technology obtains the same instantaneous three-dimensional coordinate information of a shot surface by performing high frame rate snapshot on the road surface, analyzes and obtains a color depth image of the road surface, realizes sub-millimeter high-precision measurement of ruts, is not influenced by vehicle-mounted vibration and jolt, and meets the precision requirement to the maximum extent while ensuring vehicle-mounted large-scale measurement.
Disclosure of Invention
In order to solve the problems, the invention provides a road rut fine measurement method based on structured light, which adopts the following technical scheme:
the invention provides a road rut fine measurement method based on structured light, which is characterized by comprising the following steps: s1, mounting structured light collection equipment on a detection vehicle, shooting a road surface to be detected at a overlook angle, and collecting point cloud data of the road surface; s2, preprocessing point cloud data; s3, performing two-dimensional rasterization processing on the preprocessed point cloud data; s4, extracting various features of the point cloud data in each grid, and mapping the various normalized features into gray level images respectively; s5, identifying an abnormal area in the gray image as a rut area by using an image identification method; and S6, extracting point cloud data in the rut area, and calculating the maximum depth, width, length, area and area volume of the rut area as evaluation indexes of the rut.
The method for finely measuring the road ruts based on the structured light provided by the invention can also have the technical characteristics that the road point cloud data at least contains depth information, namely the distance between the collected points and the collecting equipment.
The method for finely measuring the road ruts based on the structured light can also have the technical characteristics that the preprocessing comprises denoising and gradient removing.
The invention provides a structure-based methodThe method for fine measurement of the ruts on the optical pavement can also have the technical characteristics that the denoising comprises the following steps: s2-1a, calculating the average distance D from each point in the point cloud data to the nearest k points, and constructing a set D { D } 1 ,d 2 ,…,d n }; step S2-2a, fitting the set D { D normally 1 ,d 2 ,…,d n Calculating the mean value mu and the variance sigma; step S2-2a, get d i Exceeds [ mu-3 sigma, mu +3 sigma]And taking the points of the interval as noise points and removing the noise points.
The method for finely measuring the ruts on the road surface based on the structured light can also have the technical characteristics that the gradient removing process is as follows: s2-1b, taking a three-dimensional coordinate set O { (x) of all point cloud data 1 ,y 1 ,z 1 ),(x 2 ,y 2 ,z 2 ),…,(x n ,y n ,z n ) }; s2-2b, adopting a least square method to fit the set O to obtain a corresponding three-dimensional plane parameter k x ,k y B, b; s2-3b, calculating a three-dimensional coordinate set O' { (x) of the point cloud data after gradient removal 1 ,y 1 ,z 1 -k x x 1 -k y y 1 -b),(x 2 ,y 2 ,z 2 -k x x 2 -k y y 2 -b),…,(x n ,y n ,z n -k x x n -k y y n -b)}。
The method for fine measurement of the road ruts based on the structured light provided by the invention can also have the technical characteristics that in the step S4, the characteristic types of the point cloud data at least comprise a height value of the point cloud after slope removal, a reflection intensity value of the point cloud, a surface normal, a main curvature, a point characteristic histogram descriptor, a fast point characteristic histogram descriptor and a 3D shape content descriptor.
The method for fine measuring the ruts on the road surface based on the structured light, provided by the invention, can further have the technical characteristics that in the step S4, the mapping process is as follows: s4-1, calculating the maximum value and the minimum value of the characteristic values; and S4-2, mapping the gray value of the characteristic maximum value position to be 255, mapping the gray value of the characteristic minimum value position to be 0, mapping the rest positions to be integers of [0,255] according to a linear scale, and rounding up when the rest positions are mapped to be decimal.
The method for finely measuring the ruts on the road surface based on the structured light provided by the invention can also have the technical characteristics that the image identification method at least adopts any one of a convolutional neural network identification method or a support vector machine identification method.
The method for finely measuring the ruts on the road surface based on the structured light, provided by the invention, can also have the technical characteristics that the maximum depth of the rut area is calculated as follows: s6-1, calculating the average distance d from each point in the point cloud data of the track area to the nearest k points in the z-axis direction z All points forming a set D z {d z1 ,d z2 ,…,d zn }; step S6-2, set D is taken z The point cloud corresponding to the maximum value in the point cloud is taken as the highest point, and the z-axis coordinate of the point cloud is taken as the ground height z o (ii) a S6-3, calculating a point cloud corresponding to the minimum value of the z axis of the point cloud data of the normalized rut area as a lowest point, and taking the z axis coordinate of the point cloud data as the height z of the maximum depth of the rut 2 (ii) a Step S6-4, taking z o -z 2 As the maximum depth of the rut area.
The method for finely measuring the ruts on the road surface based on the structured light can also have the technical characteristics that the width of the rut area is obtained by fitting a minimum circumscribed rectangle of the rut area in a gray image by adopting a least square method, the length of the short side of the minimum circumscribed rectangle is taken as the width w of the rut area, the length of the rut area is the field side length l of the minimum circumscribed rectangle, the area of the rut area is wxl, and the area volume of the rut area is
In the formula (I), the compound is shown in the specification,
is the average depth, the average depth
Mean value by z-axis of cloud of points in the rut area
Taking the height z of the ground o And with
The difference of (a) is obtained.
Action and effects of the invention
According to the pavement rut fine measurement method based on the structured light, the collected pavement point cloud data is subjected to denoising, gradient removing and two-dimensional rasterization processing, various features of the rasterized point cloud data are mapped into gray images, and identification and fine measurement of ruts are achieved based on an image identification and multi-feature fusion method. According to the method, the vehicle-mounted structured light acquisition equipment is adopted for acquiring the point cloud data of the road surface, so that the measurement of the rutting degree of the road surface under the dynamic traffic flow speed can be supported, on one hand, a more precise road damage condition is provided for a road maintenance department, a basis is provided for maintenance fund distribution and measure selection, and on the other hand, a guarantee can be provided for more rapid and precise rutting detection and precise evaluation and also for trip safety. In addition, the point cloud data is subjected to a two-dimensional rasterization method, so that the calculation amount of point cloud data processing is greatly reduced.
The road rut fine measurement method based on the structured light can realize the rapid and fine measurement of the road rut at the traffic flow speed, improve the detection speed of the road rut, reduce the dependence on manual work or high-precision instruments, and has important significance on the high-frequency digital detection of the large-scale road performance.
Drawings
Fig. 1 is a flowchart of a road rut fine measurement method based on structured light according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of point cloud data after de-ramping in an embodiment of the present invention;
FIG. 3 is a schematic diagram of rasterization and grayscale mapping of point cloud data in an embodiment of the present invention.
Detailed Description
The invention relates to a road rut fine measurement method based on structured light, which mainly utilizes vehicle-mounted structured light acquisition equipment to acquire road point cloud data and realizes the identification and fine measurement of ruts through two-dimensional rasterization processing, image identification and multi-feature fusion.
In order to make the technical means, creation features, achievement objects and effects of the present invention easy to understand, the following describes the road rut fine measurement method based on the structured light in detail with reference to the embodiments and the accompanying drawings.
< example >
Fig. 1 is a flowchart of a road rut fine measurement method based on structured light in an embodiment of the present invention.
As shown in fig. 1, the road rut fine measurement method based on the structured light includes the following steps:
step S1, mounting the structured light collection equipment on a detection vehicle, shooting a road surface to be detected at an overlooking angle, and collecting point cloud data of the road surface.
In order to ensure the sampling precision and the sampling density, the structured light acquisition equipment needs to adjust the installation position, can be installed and fixed in front of or behind a detection vehicle, vertically shoots towards the ground to ensure that the longitudinal sampling precision is greater than 5mm, and the sampling density is greater than 9 ten thousand per square meter. In this embodiment, the structured light collection device is installed behind the roof of the inspection vehicle, and the lens captures images vertically downward, thereby collecting point cloud data including elevation information and reflection intensity information. Wherein, the elevation (depth) information is the distance between the acquired point and the acquisition equipment.
And S2, preprocessing the point cloud data.
In this embodiment, the point cloud data is first denoised, and the process is as follows:
s2-1a, calculating the average distance D from each point in the point cloud data to the nearest 4 points, and constructing a set D { D } 1 ,d 2 ,…,d n }；
Step S2-2a, fitting the set D { D) normally 1 ,d 2 ,…,d n And its mean value μ =15 and variance σ =1.24 are calculated;
step S2-2a, get d i Exceeds [ mu-3 sigma, mu +3 sigma]The interval is [11.28,18.72 ]]The points of (2) are taken as noise points and eliminated.
Then, carrying out grade removal processing on the point cloud data:
s2-1b, taking a three-dimensional coordinate set O { (x) of all point cloud data 1 ,y 1 ,z 1 ),(x 2 ,y 2 ,z 2 ),…,(x n ,y n ,z n )}；
S2-2b, fitting the set O by adopting a least square method to obtain a corresponding three-dimensional plane parameter k x ＝0.036,k y ＝-0.008,b＝57.031；
S2-3b, calculating a three-dimensional coordinate set O' { (x) of the point cloud data after gradient removal 1 ,y 1 ,z 1 -k x x 1 -k y y 1 -b),(x 2 ,y 2 ,z 2 -k x x 2 -k y y 2 -b),…,(x n ,y n ,z n -k x x n -k y y n -b) }, removing the point cloud data pairs before and after the slope, such as shown in fig. 2.
And S3, performing two-dimensional rasterization processing on the x and y planes of the preprocessed point cloud data.
FIG. 3 is a schematic diagram of rasterization and grayscale mapping of point cloud data in an embodiment of the present invention.
In this embodiment, the two-dimensional rasterization processing is as follows: the preprocessed point cloud data is projected to an x and y plane, and planar two-dimensional rasterization processing is performed by using a 5 × 5 grid, and the processing result is shown in fig. 3.
And S4, extracting various features of the point cloud data in each grid, and mapping the various normalized features into gray level images respectively.
Features of the point cloud data include, but are not limited to, the following categories: height value of the degraded point cloud, reflection intensity value of the point cloud, surface normal, principal curvature, point feature histogram descriptor (PFH), fast point feature histogram descriptor (FPFH), 3D shape content descriptor, and the like.
In this embodiment, the height value of the point cloud is selected as the point cloud data feature, the average height of the point cloud data in each grid is extracted, the gray level of the pixel point corresponding to the largest grid is 255, the gray level of the pixel point corresponding to the smallest grid is 0, and the gray levels of the pixel points corresponding to the remaining grids are interpolated in proportion to obtain a mapped gray level image, as shown in fig. 3.
And S5, identifying abnormal areas in the gray level image as rut areas by using an image identification method such as a convolutional neural network or a support vector machine.
In this embodiment, a cyclic local binary system (cyclic LBP) algorithm is first used to extract Local Binary Pattern (LBP) features, i.e., texture features, of the mapped gray-scale image, and a binary feature calculation formula of each pixel point is as follows:
in the formula (x) c ,y c ) Is the central pixel, i c Is the gray value, i p Is the gray value of the neighboring pixel, s is a sign function:
and then, inputting the binary characteristics into a Support Vector Machine (SVM) to classify whether the features belong to the rut areas.
And S6, extracting basic parameters of the point cloud data in the rut area, and calculating the maximum depth, width, length, area and area volume of the rut area as evaluation indexes of the rut.
In this embodiment, the basic parameters include ground height, height of the maximum depth of the rut, and average value of z-axis of point cloud in the area
Length and width of minimum circumscribed rectangle. The extraction process of these basic parameters is as follows:
the calculation process of the ground height and the height at the maximum depth is as follows:
firstly, calculating the average distance d between each point in the point cloud data of the track area and the nearest k points in the z-axis direction z All points forming a set D z {d z1 ,d z2 ,…,d zn }。
Then, take set D z The point cloud corresponding to the maximum value in the point cloud is taken as the highest point, and the z-axis coordinate of the point cloud is taken as the ground height z o 。
Finally, calculating the point cloud corresponding to the minimum value of the z axis of the point cloud data of the normalized rutting area as the lowest point, and taking the z axis coordinate as the height z of the maximum depth position of the rutting 2 。
And the minimum circumscribed rectangle is obtained by fitting the rutting area in the gray-scale image by adopting a least square method, and the length of the long side and the length of the short side of the minimum circumscribed rectangle are respectively used as the length l and the width w of the rutting area.
The results of the above basic parameters calculated in this example are shown in table 1 below:
TABLE 1
In this example, take z o -z 2 As the maximum depth of the rutting area, the area of the rutting area is w × l, and the volume of the rutting area is
Wherein, the first and the second end of the pipe are connected with each other,
by taking the ground height z for average depth o Mean value of z-axis of cloud of points in rut area
The difference of (a) is obtained.
The fine evaluation of the detected rut was calculated based on the basic parameters calculated in table 1 above, and the results are shown in table 2 below:
TABLE 2
Effects and effects of the embodiments
According to the structured light-based pavement rut fine measurement method provided by the embodiment, the collected pavement point cloud data is subjected to denoising, gradient removal and two-dimensional rasterization, various features of the rasterized point cloud data are mapped into gray images, and identification and fine measurement of ruts are realized based on an image identification and multi-feature fusion method.
In the embodiment, as the vehicle-mounted structured light acquisition equipment is adopted to acquire the point cloud data of the road surface, the measurement of the rutting degree of the road surface at the dynamic traffic flow speed can be supported, on one hand, a more precise road damage condition is provided for a road maintenance department, and a basis is provided for maintenance fund distribution and measure selection, and on the other hand, the more rapid and precise rutting detection and precise evaluation can also provide guarantee for trip safety. In addition, the point cloud data is processed by a two-dimensional rasterization method, so that the calculation amount of the point cloud data processing is greatly reduced.
In conclusion, the road rut fine measurement method based on the structured light can realize rapid and fine measurement of road ruts at the traffic flow speed, improve the detection speed of the road ruts, reduce the dependence on manual work or high-precision instruments, and has important significance for large-scale high-frequency digital detection of road performance.
The above-described embodiments are merely illustrative of specific embodiments of the present invention, and the present invention is not limited to the description of the above-described embodiments.
Claims (10)
1. A road rut fine measurement method based on structured light is characterized by comprising the following steps:
s1, mounting structured light collection equipment on a detection vehicle, shooting a road surface to be detected at a overlook angle, and collecting point cloud data of the road surface;
s2, preprocessing the point cloud data;
s3, performing two-dimensional rasterization processing on an x plane and a y plane on the preprocessed point cloud data;
s4, extracting various features of the point cloud data in each grid, and mapping the various normalized features into gray level images respectively;
s5, identifying an abnormal area in the gray image as a rutting area by using an image identification method;
and S6, extracting point cloud data in the rut area, and calculating the maximum depth, width, length, area and area volume of the rut area as evaluation indexes of the rut.
2. A structured light based pavement rut fine measurement method according to claim 1, characterized in that:
the road surface point cloud data at least comprises depth information, namely the distance between the acquired point and the acquisition equipment.
3. A structured light based pavement rut fine measurement method according to claim 1, characterized in that:
wherein the preprocessing comprises denoising and degranulation.
4. A structured light based pavement rut fine measurement method according to claim 3, characterized in that:
wherein the denoising comprises the following steps:
s2-1a, calculating the average distance D from each point in the point cloud data to the nearest k points, and constructing a set D { D } 1 ,d 2 ,…,d n }；
Step S2-2a, fitting the set D { D normally 1 ,d 2 ,…,d n Calculating the mean value mu and the variance sigma;
step S2-2a, get d i Exceed [ mu-3 sigma, mu +3 sigma]And taking the points of the interval as noise points and removing the noise points.
5. A structured light based pavement rut fine measurement method according to claim 4, characterized in that:
wherein the process of de-grading is as follows:
s2-1b, taking a three-dimensional coordinate set O { (x) of all point cloud data 1 ,y 1 ,z 1 ),(x 2 ,y 2 ,z 2 ),…,(x n ,y n ,z n )}；
S2-2b, fitting the set O by adopting a least square method to obtain a corresponding three-dimensional plane parameter k x ,k y ,b；
S2-3b, calculating a three-dimensional coordinate set O' { (x) of the point cloud data after gradient removal 1 ,y 1 ,z 1 -k x x 1 -k y y 1 -b),(x 2 ,y 2 ,z 2 -k x x 2 -k y y 2 -b),…,(x n ,y n ,z n -k x x n -k y y n -b)}。
6. A structured light based pavement rut fine measurement method according to claim 1, characterized in that:
in step S4, the feature types of the point cloud data at least include a height value of the point cloud after the gradient removal, a reflection intensity value of the point cloud, a surface normal, a principal curvature, a point feature histogram descriptor, a fast point feature histogram descriptor, and a 3D shape content descriptor.
7. A structured light based pavement rut fine measurement method according to claim 1, characterized in that:
in step S4, the mapping process is as follows:
s4-1, calculating the maximum value and the minimum value of the characteristic values;
and S4-2, mapping the gray value at the characteristic maximum value position to be 255, mapping the gray value at the characteristic minimum value position to be 0, mapping the rest positions to be integers of [0,255] according to a linear proportion, and rounding up when the rest positions are mapped to be decimal.
8. A structured light based pavement rut fine measurement method according to claim 1, characterized in that:
the image identification method at least adopts one of a convolutional neural network identification method or a support vector machine identification method.
9. A structured light based pavement rut fine measurement method according to claim 1, characterized in that:
wherein the maximum depth of the rut area is calculated as follows:
s6-1, calculating the average distance d from each point in the point cloud data of the track area to the nearest k points in the z-axis direction z All points forming a set D z {d z1 ,d z2 ,…,d zn }；
Step S6-2, set D is taken z The point cloud corresponding to the maximum value in the (1) is taken as the highest point, and the z-axis coordinate of the point cloud is taken as the ground height z o ；
S6-3, calculating the point cloud corresponding to the z-axis minimum value of the normalized point cloud data of the rutting area as the lowest point, and taking the z-axis coordinate of the point cloud data as the height z of the rutting maximum depth position 2 ；
Step S6-4, taking z o -z 2 As the maximum depth of the rut area.
10. A method for fine measurement of ruts on a pavement based on structured light as claimed in claim 9, wherein:
wherein the width of the rutting area is obtained by fitting a minimum bounding rectangle of the rutting area in the gray-scale image by adopting a least square method, and the length of the short side of the minimum bounding rectangle is taken as the width w of the rutting area,
the length of the rut area is the field side length l of the minimum circumscribed rectangle,
the area of the rut area is w x l,
the area volume of the rut area is
Wherein the content of the first and second substances,
is the average depth, the average depth
Mean value through the z-axis of the cloud of points in the rut area
Taking the height z of the ground o And with
The difference of (a) is obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210785268.5A CN115164762A (en) | 2022-07-04 | 2022-07-04 | Pavement rut fine measurement method based on structured light |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210785268.5A CN115164762A (en) | 2022-07-04 | 2022-07-04 | Pavement rut fine measurement method based on structured light |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115164762A true CN115164762A (en) | 2022-10-11 |
Family
ID=83491295
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210785268.5A Pending CN115164762A (en) | 2022-07-04 | 2022-07-04 | Pavement rut fine measurement method based on structured light |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115164762A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115908526A (en) * | 2022-11-24 | 2023-04-04 | 深圳市城市交通规划设计研究中心股份有限公司 | Rut length calculation method based on three-dimensional reconstruction of road rut diseases |
-
2022
- 2022-07-04 CN CN202210785268.5A patent/CN115164762A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115908526A (en) * | 2022-11-24 | 2023-04-04 | 深圳市城市交通规划设计研究中心股份有限公司 | Rut length calculation method based on three-dimensional reconstruction of road rut diseases |
| CN115908526B (en) * | 2022-11-24 | 2023-08-18 | 深圳市城市交通规划设计研究中心股份有限公司 | Track length calculation method based on three-dimensional reconstruction of pavement track diseases |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108319920B (en) | Road marking detection and parameter calculation method based on line scanning three-dimensional point cloud | |
| CN112465948B (en) | Vehicle-mounted laser pavement point cloud rarefying method capable of retaining spatial features | |
| CN110780307B (en) | Method for obtaining road cross section based on storage battery car-mounted laser point cloud mobile measurement system | |
| CN112070756B (en) | Three-dimensional road surface disease measuring method based on unmanned aerial vehicle oblique photography | |
| CN103778429A (en) | Method for automatically extracting road information in vehicle-mounted laser scanning point cloud | |
| EP4120123A1 (en) | Scan line-based road point cloud extraction method | |
| CN110473187B (en) | Object-oriented line scanning three-dimensional pavement crack extraction method | |
| CN111274843A (en) | Truck overload monitoring method and system based on monitoring video | |
| CN106978774A (en) | A kind of road surface pit automatic testing method | |
| CN108765376B (en) | Linear scanning three-dimensional pavement data component analysis method | |
| CN105133471A (en) | Linear structured light pavement surface detection system-based pavement depth image production method | |
| CN111968079B (en) | Three-dimensional pavement crack extraction method based on local extremum of section and segmentation sparsity | |
| CN114049618B (en) | Graph-point-graph transformation-based pavement three-dimensional disease PCI calculation method | |
| CN115164762A (en) | Pavement rut fine measurement method based on structured light | |
| CN105069395A (en) | Road marking automatic identification method based on terrestrial three-dimensional laser scanning technology | |
| CN113218310A (en) | Extraction method and system of important parameters of dry beach of tailing pond based on three-dimensional laser point cloud | |
| JP6929207B2 (en) | Road image processing device, road image processing method, road image processing program, and recording medium | |
| CN115797338B (en) | Panoramic pavement multi-performance index calculation method and system based on binocular vision | |
| CN112964195A (en) | Power supply rail geometric parameter comprehensive detection method and system based on laser triangulation | |
| Li et al. | An efficient method for automatic road extraction based on multiple features from LiDAR Data | |
| Gan et al. | Sub-pixel extraction of laser stripe in complex background | |
| CN116242774A (en) | Light three-dimensional detection method and device suitable for rural highways | |
| CN114063107A (en) | Ground point cloud extraction method based on laser beam | |
| CN113643232A (en) | Pavement pit automatic detection method based on binocular camera and convolutional neural network | |
| CN114299247A (en) | Rapid detection and problem troubleshooting method for road traffic sign lines |
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 |