CN111583362A - Visual recording method for detecting asphalt pavement disease condition - Google Patents
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Abstract
The invention discloses a visual recording method for manual detection of asphalt pavement disease conditions, which comprises the following steps: selecting an approximate edge shape according to a disease division mode in the specification, and establishing an MATLAB file of a damage unit generation rule; establishing an Excel recording rule of type data, size data and position data of the damaged unit in the manual detection data; and importing the original record table into MATLAB, generating a damage unit according to the type data, configuring the size of the unit according to the size data, and generating a unit coordinate according to the position information to generate a hectometer damage graphic representation of the asphalt pavement. The method is mainly used for the visual recording of the artificial detection of the disease condition of the asphalt pavement, is convenient for the data collection and statistics of the pavement disease investigation, and forms the visual original data of the pavement of the artificial routing inspection. The actual disease condition of the asphalt pavement is reflected in a visualized mode, so that management workers can see visual pavement condition images, the pavement degradation process can be deeply known, and the pavement service performance can be correctly evaluated.
Description
Technical Field
The invention belongs to the technical field of detection and evaluation of the use condition of an asphalt pavement, and is used for manual inspection and data recording of the disease condition of the asphalt pavement; in particular to a visual recording method for detecting the disease condition of an asphalt pavement.
Background
At the present stage, a large amount of manual detection is still needed for detecting the use condition of the asphalt pavement, data acquisition is generally carried out by taking 'assessment standards of road technical conditions' as a guide, the data result is 'an asphalt pavement damage questionnaire' obtained after secondary calculation by inspection personnel, and the problems of poor traceability, poor accuracy and poor stability and the like exist in the detected data result. Researchers and managers can only research and make decisions through the segmented calculation results of the unit detection data, and the actual use condition of the asphalt pavement is lack of intuitive, specific and visual grasp.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems of the conventional routing inspection recording mode, the visual recording method for the asphalt pavement disease condition detection data provided by the invention can truly reduce the asphalt pavement disease condition on the premise of not deviating from the standard requirement, and realizes data visualization of the asphalt pavement disease condition of the evaluation layer. According to the recording method, original visual data of manual inspection are stored on the premise that the workload of inspection personnel is not increased, statistics and tracing of data are facilitated, management workers can see an intuitive road surface condition image, and diversification of road surface detection data is improved.
The technical scheme is as follows: in order to realize the purpose of the invention, the invention establishes a visual recording method for detecting the disease condition of the asphalt pavement, an algorithm automatically generates a hectometer damage graphic representation of the asphalt pavement in MATLAB according to an original detection record and generates a road section statistical result required by a standard, and the method specifically comprises the following steps:
(1) and selecting the approximate edge shape of the asphalt pavement disease unit according to the type and degree of the asphalt pavement disease, and establishing an asphalt pavement damage type basic unit by using MATLAB.
(2) The method comprises the steps of establishing an original record table of the asphalt pavement damage condition by utilizing Excel, inputting the type and degree of a damage unit and the position information of a central point in a pull-down menu mode, and inputting the size information of the damage unit in a manual mode.
(3) A100 multiplied by L rectangular hectometer pavement unit is built by adopting MATLAB, wherein L is the width of a pavement, X direction is the driving direction, Y direction is the cross section direction, and the hectometer pavement unit is divided into an upper part, a middle part and a lower part along the X direction and is used for configuring and positioning a damaged unit.
(4) And importing an original record table of the damage condition of the asphalt pavement into MATLAB, configuring damage unit information, generating corresponding damage units on hectometer pavement units according to the type, degree, central point position and size data of the damage units, and establishing a hectometer damage diagram of the asphalt pavement.
In the step (1), MATLAB is adopted to compile an asphalt pavement damage type basic unit generation file, the required parameters are [ X _ ZB, Y _ ZB, Length, Width and depth ], which respectively represent X coordinates, Y coordinates, Length, Width and severity, the asphalt pavement damage unit basic types are divided into 7 categories, and the judgment standard and the basic unit generation method are as follows:
(1-1) cracking: the crack block degree is 0.2-0.5 m, and the average crack width is less than 2mm, so that the crack is slight; the crack block size is less than 0.2m, and the average crack width is 2-5 mm, so that the crack is moderate; the cracks have a block size of less than 0.2m and an average crack width of more than 5mm, and are severe cracks.
And (3) approximately converting the disease unit into a quadrangle, and generating a blue polygon edge boundary by adopting an MATLAB program, wherein the Length is Length, and the Width is Width. Generating random cracks by adopting a UNIFIND function, carrying out grid filling on the generated random transverse/longitudinal cracks with the crack block degree of 0.2-0.5 m and the crack block degree of less than 0.2m, and selecting the following 2 filling colors: pink ([1,0.75294,0.79608]), dark pink ([1,0.07843,0.57647]), in that order, indicates slight cracking, moderate/severe cracking.
(1-2) bulk cracking: the main crack has the bulk degree larger than 1.0m, and the average crack width is 1-2 mm, so that the crack is a slight bulk crack; the main crack block degree is 0.5-1.0 m, and the severe block cracks are formed when the average crack width is more than 2 mm.
And (3) approximately converting the disease unit into a rectangle, generating a blue polygon edge boundary by adopting an MATLAB program, configuring the Length and the Width according to the recorded data, wherein the Length is Length, and the Width is Width. A green dotted line is selected to bias inwards to generate a block crack diagram, the bias distances are 0.5m and 1m respectively, and light block cracks and heavy block cracks are represented.
(1-3) cracking: the longitudinal cracks are basically parallel to the driving direction, the transverse cracks are basically vertical to the driving direction, the severity degree takes the crack width of 3mm as a boundary, the crack width of more than 3mm is a severe crack, and the crack width of less than 3mm is a mild crack.
And compiling a random crack generation function by using a UNIFIND function, and performing unit configuration by using a Length to generate a longitudinal/transverse crack diagram. Longitudinal cracks were created using green lines and transverse cracks were created using sky blue ([0,0.74902,1]) lines. The mild crack linewidth is configured as a default pixel and the severe crack linewidth is configured as 2 pixels.
(1-4) pit: the depth of the light pit is less than 25mm, or the area of the light pit is less than 0.1m 2; the depth of the heavy pit is more than 25mm, or the area of the heavy pit is more than 0.1m 2.
Generally the pits have a diffuse form of circular or elliptical shape with an area of less than 0.1m2, and a diameter of approximately less than 0.178 m. The major axis Length is configured according to the Length and Width of the pit, an orange ([1,0.54902,0]) elliptical edge boundary is generated, a light pit is filled by a red major axis, and a heavy pit is filled by a blue major axis.
(1-5) repairing: the patching adopts a rectangular conversion area mode, rectangular units are generated according to the size information Length and Width, and the patching units are filled with gray ([0.7451,0.7451,0.7451]) surfaces.
(1-6) generating other surface defect diseases such as oil bleeding, loosening and the like by uniformly adopting rectangular conversion area and position information, generating blue rectangles according to the Length and Width of the sizes, filling the oil bleeding disease units with random circles, and filling the loosening with random diamonds.
(1-7) uniformly performing surface deformation damage such as subsidence, rutting, hugging and the like by adopting a rectangular conversion mode, performing unit configuration according to size information Length and Width to generate a black rectangular boundary, and internally filling a surface deformation damage unit by adopting a red curve.
In the step (2), an original recording mode is provided in an Excel table form, an 'asphalt pavement damage condition original recording table' is created, wherein length and width information of the damaged unit is manually input, a pull-down menu is established through an Excel data verification mode according to the type, degree and center point position information of the damaged unit, limited options are set, and corresponding options are selected for inputting.
When creating the "original record table of the damaged condition of the asphalt pavement", the options for defining the types of the damaged units are set as follows: crack diseases, block cracks, longitudinal cracks, transverse cracks, sinking diseases, track diseases, wave congestion, pit slot diseases, loosening diseases, oil bleeding diseases and repairing diseases.
The disease location restriction options are set as follows: a left driving belt, a right driving belt, a driving lane central line, a left edge line and a right edge line (Y direction); upper, middle, lower (X direction).
In the step (3), the X coordinate range of the hectometer pavement unit is X e < -50, the pavement unit is divided into an upper part, a middle part and a lower part along the X direction, the upper coordinate range is X e < -50, -20 >, the middle coordinate range is X e < -20, 20 >, and the lower coordinate range is X e < -20, 50.
In the step (4), the hundred-meter damage graphic representation of the asphalt pavement is determined by the following steps:
(4-1) reading the survey data in the "asphalt pavement damage condition original record table" by using MATLAB, reading the damaged cell size and shape data raw (i,5), judging the measurement unit of the damaged cell, and extracting the length information "length _ i", the width information "width _ i" and the degree information "degree".
(4-2) generating a damaged cell center point X coordinate: judging the longitudinal positioning data of the central point position information recorded by raw (i,8), determining the X coordinate range of the damaged unit, and generating a specific X-coordinate value matrix 'X _ ZB' of the damaged unit on a hectometer pavement unit and a drawing breadth position (upper/middle/lower part) by adopting a distance constraint mode of '2 (X-Xi) > (length h + length hi').
(4-3) generating a damaged cell center point Y coordinate: and judging the cross section positioning data (left/right wheel track, center line of the traffic lane, left/right edge) of the central point position information recorded by raw (i,9) to generate a central point y coordinate value matrix 'y _ ZB' of the damaged unit.
And (4-4) reading the type data of the damaged unit, verifying the damaged type data recorded in raw (i,2) and generating a damaged unit type judgment result matrix 'void'.
And (4-5) calling the MATLAB damage type basic unit generation file corresponding to the parameter of [ X _ ZB _ i, Y _ ZB _ i, Length _ i, Width _ i and depth _ i ] read in the steps (13) to (16) to generate a hectometre damage diagram of the asphalt pavement.
Has the advantages that: compared with the prior art, the technical scheme of the invention has the following beneficial technical effects: the method can assist the detection personnel to store the original inspection data, is convenient for collecting and recording the data, simultaneously truly reduces the disease condition of the asphalt pavement, generates a hectometer damage diagram of the asphalt pavement, and provides a visual scheme for the manual detection mode of the disease investigation of the asphalt pavement.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a bituminous pavement (L ═ 7m) hectometer pavement unit;
FIG. 3 is a graphical representation of one hundred meter damage for an asphalt pavement (L ═ 7 m);
fig. 4 is a bituminous pavement (L ═ 4m) hectometer pavement unit;
fig. 5 is a graphical representation of hectometer damage to an asphalt pavement (L ═ 4 m).
Detailed Description
The technical solutions of the present invention are described in detail below with reference to the drawings and the embodiments, but the scope of the present invention is not limited to the embodiments.
Example 1
The method is implemented in the artificial inspection of the disease condition of the asphalt pavement of a certain double lane with the width of 7 meters:
1. selecting an approximate edge shape of an asphalt pavement disease unit according to the type and degree of the asphalt pavement disease, establishing an asphalt pavement disease type basic unit by using MATLAB, compiling an asphalt pavement disease type basic unit generation file, setting input parameters of [ X _ ZB, Y _ ZB, Length, Width, depth ], respectively representing an X coordinate, a Y coordinate, a Length, a Width and a severity, and dividing the basic types of the asphalt pavement damage units into 7 classes, wherein the generation method of each basic unit comprises the following steps:
(1) cracking: and (3) approximately converting the disease unit into a quadrangle, and generating a blue polygon edge boundary by adopting an MATLAB program, wherein the Length is Length, and the Width is Width. Generating random cracks by adopting a UNIFIND function, carrying out grid filling on the generated random transverse/longitudinal cracks with the crack block degree of 0.2-0.5 m and the crack block degree of less than 0.2m, and selecting the following 2 filling colors: pink ([1,0.75294,0.79608]), dark pink ([1,0.07843,0.57647]), in that order, indicates slight cracking, moderate/severe cracking.
(2) Block cracking: and (3) approximately converting the disease unit into a rectangle, generating a blue polygon edge boundary by adopting an MATLAB program, configuring the Length and the Width according to the recorded data, wherein the Length is Length, and the Width is Width. A green dotted line is selected to bias inwards to generate a block crack diagram, the bias distances are 0.5m and 1m respectively, and light block cracks and heavy block cracks are represented.
(3) And (3) cracking: and compiling a random crack generation function by using a UNIFIND function, and performing unit configuration by using a Length to generate a longitudinal/transverse crack diagram. Longitudinal cracks were created using green lines and transverse cracks were created using sky blue ([0,0.74902,1]) lines. The mild crack linewidth is configured as a default pixel and the severe crack linewidth is configured as 2 pixels.
(4) Pit and groove: the major axis Length is configured according to the Length and Width of the pit, an orange ([1,0.54902,0]) elliptical edge boundary is generated, a light pit is filled by a red major axis, and a heavy pit is filled by a blue major axis.
(5) Repairing: the patching adopts a rectangular conversion area mode, rectangular units are generated according to the size information Length and Width, and the patching units are filled with gray ([0.7451,0.7451,0.7451]) surfaces.
(6) And the rest surface defect diseases such as oil bleeding, loosening and the like are generated uniformly by adopting rectangle conversion area and position information, blue rectangles are generated according to the sizes of Length and Width, oil bleeding damage units are filled inside by random circles, and loosening is filled inside by random diamonds.
(7) And uniformly performing surface deformation and damage such as subsidence, rutting, congestion and the like by adopting a rectangular conversion mode, performing unit configuration according to the size information Length and Width to generate a black rectangular boundary, and internally filling the surface deformation and damage units by adopting red curves.
2. The method comprises the steps of establishing an original recording table of the asphalt pavement damage condition by using Excel, recording the type, degree and central point position information of a damaged unit in a pull-down menu mode, recording the size information of the damaged unit in a manual mode, and recording the result as shown in table 1.
TABLE 1 original record of asphalt pavement damage condition
3. A100 m multiplied by 7m rectangular hectometer pavement unit is built by MATLAB, the X direction is the traffic direction, the Y direction is the cross section direction, the hectometer pavement unit is divided into an upper part, a middle part and a lower part along the X direction and is used for configuring and positioning a damaged unit, the upper coordinate range is X epsilon < -50 > -20 >, the middle coordinate range is X epsilon < -20 >, and the lower coordinate range is X epsilon < -20 > -50 ], as shown in figure 2.
4. The investigation data in table 1 is read using MATLAB, the damaged cell size shape data raw (i,5) is read, the unit of measure of the damaged cell is judged, and the length information "length _ i", the width information "width _ i", and the degree information "degree" are extracted.
Judging the longitudinal positioning data of the central point position information recorded by raw (i,8), determining the X coordinate range of the damaged unit, and generating a specific X-coordinate value matrix 'X _ ZB' of the damaged unit on a hectometer pavement unit and a drawing breadth position (upper/middle/lower part) by adopting a distance constraint mode of '2 (X-Xi) > (length h + length hi').
And judging the cross section positioning data (left/right wheel track, center line of the traffic lane, left/right edge) of the central point position information recorded by raw (i,9) to generate a central point y coordinate value matrix 'y _ ZB' of the damaged unit.
And reading the type data of the damaged unit, verifying the damaged type data recorded by raw (i,2) and generating a damaged unit type judgment result matrix 'hierarchy'.
Table 2 parameters required for generating a file by a corrupt unit
| Numbering | x | y | Length | Width | degree |
| 1 | -6.4451 | 5.25 | 3 | 3 | Light and lightweight |
| 2 | 41.7960 | 4.00 | 2 | 0.5 | Light and lightweight |
| 3 | 46.5604 | 4.00 | 2 | 0 | |
| 4 | 0.4304 | 4.00 | 3 | 2 | In |
| 5 | -30.7717 | 3.50 | 3.5 | 0 | Light and lightweight |
| 6 | 14.0717 | 5.25 | 3 | 0.8 | Light and lightweight |
| 7 | 48.0709 | 5.25 | 0.5 | 1 | Light and lightweight |
| 8 | 25.5634 | 3.50 | 2 | 0 | Heavy load |
| 9 | -37.0244 | 5.25 | 2 | 0.3 | Heavy load |
| 10 | -23.3521 | 5.25 | 5 | 3 | Light and lightweight |
| 11 | -44.2013 | 4.00 | 1 | 2 | Light and lightweight |
| 12 | -34.4465 | 6.50 | 2 | 0 | Light and lightweight |
| 13 | 7.0464 | 6.50 | 0.4 | 0.2 | Light and lightweight |
| 14 | 31.7471 | 4.00 | 0.3 | 0.3 | |
| 15 | 18.1866 | 6.50 | 1 | 0.5 | Light and lightweight |
5. And calling the damage type basic unit to generate a file by taking the generated information matrixes X _ ZB, Y _ ZB, Length, Width and depth as parameters, and generating a hectometer damage diagram of the asphalt pavement as shown in FIG. 3.
Example 2
The method is implemented in the artificial inspection of the disease condition of a single-lane asphalt pavement with the width of 4 meters:
1. same as in step 1 of example 1.
2. The table 3 shows the results of creating a "table of original record of the damaged condition of the asphalt pavement" by using Excel.
TABLE 3 original record of asphalt pavement damage condition
3. MATLAB is adopted to establish a 100m multiplied by 4m rectangular hectometer pavement unit which is divided into an upper part, a middle part and a lower part, as shown in figure 4.
4. And reading the investigation data configuration unit information in the table 3 by using MATLAB to generate damaged unit parameters as shown in the table 4.
Table 4 parameters required for the corrupt unit to generate a file
| Numbering | x | y | | Width | degree | |
| 1 | 23.1937 | 3.00 | 1 | 0.5 | Light and lightweight | |
| 2 | -33.7511 | 3.50 | 2 | 0.5 | Light and lightweight | |
| 3 | -40.4669 | 3.00 | 0.3 | 0.5 | |
|
| 4 | 37.4312 | 1.00 | 3 | 2 | In | |
| 5 | 47.2081 | 2.25 | 3.5 | 0 | Light and lightweight | |
| 6 | 10.8742 | 2.25 | 3 | 0.8 | Light and lightweight | |
| 7 | 0.2150 | 1.00 | 0.5 | 1 | Light and lightweight | |
| 8 | 30.1787 | 1.50 | 2 | 0.5 | Heavy load | |
| 9 | -22.5280 | 2.25 | 2 | 0.8 | Heavy load | |
| 10 | -46.1073 | 3.00 | 3 | 1 | Light and lightweight | |
| 11 | 18.7188 | 2.25 | 2.5 | 0 | Light and lightweight | |
| 12 | 43.3990 | 1.50 | 2 | 0 | Light and lightweight | |
| 13 | -27.8362 | 2.25 | 3 | 3 | Light and lightweight | |
| 14 | -15.4444 | 1.50 | 1.5 | 0 | Heavy load | |
| 15 | -11.7925 | 2.25 | 1 | 0.5 | Light and lightweight |
5. And calling the damage type basic unit to generate a file by taking the generated information matrixes X _ ZB, Y _ ZB, Length, Width and depth as parameters, and generating a hectometer damage diagram of the asphalt pavement as shown in FIG. 5.
Comparing the original record table with the parameter generation table and the hundred meter damage graph, it can be seen that: in the link of testing the asphalt pavement damage investigation data with different widths, the asphalt pavement damage investigation visualization method can quickly and accurately reduce the damage graphic representation of the asphalt pavement, and the accuracy and universality of the method are proved.
The technical means disclosed in the invention scheme are not limited to the technical means disclosed in the above embodiments, but also include the technical scheme formed by any combination of the above technical features. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and such improvements and modifications are also considered to be within the scope of the present invention.
Claims (5)
1. A visual recording method for detecting asphalt pavement disease conditions is characterized by comprising the following steps:
(1) selecting the edge shape of the asphalt pavement disease unit according to the type and degree of the asphalt pavement disease, and establishing an asphalt pavement damage type basic unit by using MATLAB;
(2) creating an original record table of the asphalt pavement damage condition by using Excel, inputting the type and degree of a damaged unit and the position information of a central point in a pull-down menu mode, and inputting the size information of the damaged unit in a manual mode;
(3) adopting MATLAB to establish a 100 multiplied by L rectangular hectometer pavement unit, wherein L is the width of a pavement, X direction is the driving direction, Y direction is the cross section direction, and the hectometer pavement unit is divided into an upper part, a middle part and a lower part along the X direction and is used for configuring and positioning a damaged unit;
(4) and importing an original record table of the damage condition of the asphalt pavement into MATLAB, configuring damage unit information, generating corresponding damage units on hectometer pavement units according to the type, degree, central point position and size data of the damage units, and establishing a hectometer damage diagram of the asphalt pavement.
2. The visual recording method for detecting the disease condition of the asphalt pavement according to claim 1, wherein in the step (1), MATLAB is used to write basic unit generating files of the damage type of the asphalt pavement, the required parameters are [ X _ ZB, Y _ ZB, Length, Width, depth ], which respectively represent X coordinate, Y coordinate, Length, Width and severity, the basic types of the damage units of the asphalt pavement are classified into 7 categories, and the criteria and the basic unit generating method are as follows:
(1-1) cracking: the crack block degree is 0.2-0.5 m, and the average crack width is less than 2mm, so that the crack is slight; the crack block size is less than 0.2m, and the average crack width is 2-5 mm, so that the crack is moderate; the crack block size is less than 0.2m, and the average crack width is more than 5mm, so that the crack is heavily cracked;
converting the disease unit into a quadrangle, and generating a blue polygon edge boundary by adopting an MATLAB program, wherein the Length is Length, and the Width is Width; generating random cracks by adopting a UNIFIND function, carrying out grid filling on the generated random transverse/longitudinal cracks with the crack block degree of 0.2-0.5 m and the crack block degree of less than 0.2m, and selecting the following 2 filling colors: pink ([1,0.75294,0.79608]), dark pink ([1,0.07843,0.57647]), representing mild cracking, moderate/severe cracking in that order;
(1-2) bulk cracking: the main crack has the bulk degree larger than 1.0m, and the average crack width is 1-2 mm, so that the crack is a slight bulk crack; the main crack block degree is 0.5-1.0 m, and the severe block cracks are formed when the average crack width is more than 2 mm;
converting the disease unit into a rectangle, generating a blue polygon edge boundary by adopting an MATLAB program, configuring the Length and the Width according to the recorded data, wherein the Length is Length, and the Width is Width; selecting a green dotted line to bias inwards to generate a block crack diagram, wherein the bias distances are 0.5m and 1m respectively, and the block crack diagram represents slight block crack and severe block crack;
(1-3) cracking: the longitudinal cracks are parallel to the driving direction, the transverse cracks are vertical to the driving direction, the severity degree takes the crack width of 3mm as a boundary, the cracks with the width more than 3mm are severe cracks, and the cracks with the width less than 3mm are mild cracks;
compiling a random crack generation function by using a UNIFIND function, and performing unit configuration by using a Length to generate a longitudinal/transverse crack diagram; selecting green lines to generate longitudinal cracks, and selecting sky blue ([0,0.74902,1]) lines to generate transverse cracks; the line width of the slight crack is configured as a default pixel, and the line width of the severe crack is configured as 2 pixels;
(1-4) pit: the depth of the light pit is less than 25mm, or the area is less than 0.1m2(ii) a The depth of the heavy pit is more than 25mm, or the area is more than 0.1m2；
The diffusion form of the pit groove is circular or elliptical, and the area is less than 0.1m2Approximately less than 0.178m in diameter; the spindle Length is set according to the Length and Width of the pit slot to generate orange color ([1,0.54902, 0)]) The edge of the oval is limited, the light pit is filled by adopting a red main shaft, and the heavy pit is filled by adopting a blue main shaft;
(1-5) repairing: the method comprises the steps of generating rectangular units according to size information Length and Width by adopting a rectangular conversion area mode for repairing, and filling the repairing units by adopting gray ([0.7451,0.7451,0.7451]) surfaces;
(1-6) uniformly generating other surface defect diseases by adopting rectangle conversion area and position information, generating blue rectangles according to the sizes of Length and Width, filling the inside of the oil-flooding disease unit in a random circle mode, and filling the inside of the oil-flooding disease unit in a loose mode in a random rhombus mode;
and (1-7) uniformly performing surface deformation damage by adopting a rectangular conversion mode, performing unit configuration according to the size information Length and Width to generate a black rectangular boundary, and performing internal filling on the surface deformation damage unit by adopting a red curve.
3. The visual recording method for the artificial detection of the asphalt pavement damage condition as claimed in claim 1, characterized in that in step (2), an original recording mode is provided in the form of Excel table, and an "original recording table of asphalt pavement damage condition" is created, wherein the length and width information of the damage unit is manually entered, the type, degree and center point position information of the damage unit are used to establish a pull-down menu through Excel data verification mode, set the limited option, select the corresponding option to enter;
when creating the "original record table of the damaged condition of the asphalt pavement", the options for defining the types of the damaged units are set as follows: cracking diseases, block cracks, longitudinal cracks, transverse cracks, sinking diseases, track diseases, wave congestion, pit slot diseases, loosening diseases, oil bleeding diseases and repairing diseases;
the disease location restriction options are set as follows: a left driving belt, a right driving belt, a driving lane central line, a left edge line and a right edge line (Y direction); upper, middle, lower (X direction).
4. The visual recording method for detecting the disease state of asphalt pavement according to claim 1, wherein in the step (3), the X-coordinate range of the hectometer pavement unit is X e-50, the hectometer pavement unit is divided into an upper part, a middle part and a lower part along the X direction, the upper coordinate range is X e-50, -20, the middle coordinate range is X e-20, and the lower coordinate range is X e [20, 50 ].
5. The visual recording method for detecting the disease condition of the asphalt pavement as claimed in claim 1, wherein in the step (4), the hundred meter damage graphic representation of the asphalt pavement is determined by the following steps:
(4-1) reading survey data in an original record table of the asphalt pavement damage condition by using MATLAB, reading size and shape data raw (i,5) of a damaged unit, judging a measuring unit of the damaged unit, and extracting length information 'length _ i', width information 'width _ i' and degree information 'degree';
(4-2) generating a damaged cell center point X coordinate: judging the longitudinal positioning data of the central point position information recorded by raw (i,8), determining the X coordinate range of the damaged unit, the drawing positions (upper/middle/lower part) of the damaged unit on the hectometer pavement unit, and adopting 2 (X-X) as the specific X coordinate matrix of the damaged unit central point X-ZBi)>(length+lengthi) "spacing constraint mode generation;
(4-3) generating a damaged cell center point Y coordinate: judging the cross section positioning data (left/right side wheel track, center line of traffic lane, left/right side edge) of the central point position information recorded by raw (i,9) to generate a central point y coordinate value matrix 'y _ ZB' of the damaged unit;
(4-4) reading the type data of the damaged unit, verifying the damaged type data recorded in raw (i,2) and generating a damaged unit type judgment result matrix 'void';
and (4-5) calling the MATLAB damage type basic unit generation file corresponding to the parameter [ X _ ZB _ i, Y _ ZB _ i, Length _ i, Width _ i and depth _ i ] read in the steps (4-4) to generate a hectometre damage diagram of the asphalt pavement.
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