CN107341794A - Bituminous mixture laying uniformity real-time detection method - Google Patents
Bituminous mixture laying uniformity real-time detection method Download PDFInfo
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Abstract
The invention discloses a kind of bituminous mixture laying uniformity real-time detection method, and colored digital image is gathered in real time using LabVIEW softwares；And colored digital image is handled using MATLAB softwares；Based on the asphalt more than 9.5mm particle diameters to gather materials in the image after processing, the equally distributed computation model of imaged particles is established by the way of asking the side of image four static moment then to try to achieve the coefficient of variation, evaluation criterion value and evaluation result are drawn after calculating, evaluation result gives remote control center server by wireless network transmissions, by giving PC ends by wireless network Real-time Feedback after remote control center server process, realize and bituminous mixture laying uniformity is detected in real time.The present invention can be quick, convenient, real-time, quantitative detection bituminous mixture laying uniformity, and the real-time Transmission of data can be realized, ensure the accuracy and promptness of testing result.
Description
Technical field
The present invention relates to bituminous mixture laying uniformity detection technique field, more particularly to a kind of bituminous mixture laying
Uniformity real-time detection method.
Background technology
Hot-mixed bitumen pavement is in paving process, due to various, may produce bitumen content
Deviate or/and thickness is gathered materials skewness phenomenon, i.e., so-called segregation phenomenon.Asphalt mixture segregation will cause actual road surface
Mixture gradation and bitumen content substantial deviation design load, cause bituminous paving total quality uneven, can not only induce pitch
All kinds of Random early Detections occur for road surface, and road pavement Long-Term Properties also have a major impact.
At present, the pave detection method of uniformity of asphalt pavement mixture mainly has visual identity, sand patch test and nucleon
Three kinds of Density Measuring Instrument.The subjectivity of visual identity is too strong, lacks unified standard；Sand patch test principle is simple, and measurement is convenient, but pole
It is time-consuming；Nucleus Density Apparatus has certain limitation, and testing result dispersion degree is larger.Obviously, prior art bituminous paving mixes
Close material and pave that unified standard, extremely time-consuming and testing result dispersion degree are larger etc. to ask there is lacking for the detection method of uniformity
Topic.
The content of the invention
In view of this, it is an object of the invention to provide a kind of bituminous mixture laying uniformity real-time detection method, energy
Enough quick, convenient, real-time, quantitative detection bituminous mixture laying uniformities, and the real-time Transmission of data can be realized, ensure inspection
Survey the accuracy and promptness of result.
The bituminous mixture laying uniformity real-time detection method of the present invention, comprises the following steps：
A. digital camera is gathered in real time in asphalt remixer paved mixture mistake using LabVIEW softwares at PC ends
The colored digital image of captured in real-time in journey, and given the colored digital image real-time Transmission collected remotely by wireless network
Control centre's server；
B. the colored digital image collected is carried out being converted into gray level image, gray-scale map using MATLAB softwares at PC ends
As filtering and noise reduction, histogram equalization, gray level image conversion bianry image, little particle filtering, Image erosion, space fill up, be small
Particle is refiltered, image is split, image expansion is handled；
C. based on the asphalt more than 9.5mm particle diameters to gather materials in the image after processing, using to the side of image four
Ask static moment then to try to achieve the mode of the coefficient of variation and establish the equally distributed computation model of imaged particles, computation model is carried out to image
Evaluation index value and evaluation result are drawn after calculating, evaluation result gives remote control center server by wireless network transmissions,
By giving PC ends by wireless network Real-time Feedback after remote control center server process, realize equal to bituminous mixture laying
Even property detects in real time.
Further, in step a, by the corresponding IMAQ window of Labview software programmings, and Matlab images are utilized
Processing script file is combined with Labview softwares, to realize the real-time collection of colored digital image and the colour to collecting
Digital image carries out Treatment Analysis.
Further, in step a, shooting height and illumination captured in real-time asphalt remixer of the digital camera to set
Colored digital image during paved mixture, the area for the paved mixture that every colored digital image is gathered are
720mm×720mm。
Further, in step b, the conversion formula that colored digital image is converted into each pixel in gray level image is：
Gray=0.299 × R+0.587 × G+0.114 × B, Gray is gray scale in formula, and R is red channel, and G is logical for green
Road, B are blue channel.
Further, in step b, it is filled after little particle filtering using imfill function pair little particles, while use and divide
The little particle being bonded in image is separated and then filtered off, the face of more than the 9.5mm finally given aggregate particle by water ridge algorithm
The ratio that product accounts for whole figure is 40~50 ﹪.
Further, the wireless network uses 4G wireless networks, realizes the real-time Transmission of data.
Further, in step c, it is assumed that aggregate particle is shaped as circle, establishes computation model and comprises the following steps：
C1. the area of each aggregate particle and each aggregate particle distance in bianry image after treatment are counted
The distance on the side of image four, obtaining aggregate particle area in image respectively, to the static moment on four sides and the average value of each static moment, it is counted
Calculating formula is：
In formula：st1(i)、st2(i)、st3(i)、st4(i)、The area for representing i-th of aggregate particle respectively aligns
Square chart as four while (when 1,2 while, 3 while and 4 sides) ask the value of static moment and the average value of each static moment；I-th in s (i) representative images
The area of individual aggregate particle；l1(i)、l2(i)、l3(i)、l4(i) i-th of aggregate particle range image four in bianry image is represented
The distance on side；
C2. the coefficient of variation of four static moments is obtained by the static moment on four sides and the average value of each static moment, its calculation formula is：
In formula：CvFor the coefficient of variation；
C3. the uniformity that aggregate particle is distributed in image, i.e. coefficient of variation C are weighed with the coefficient of variation obtainedvValue exists
Image between section [0-1.5%], then aggregate particle distribution is more uniform；If image is more than coefficient of variation CvZhi Gai areas
Between, then aggregate particle distributing homogeneity is relatively poor.
Beneficial effects of the present invention：The bituminous mixture laying uniformity real-time detection method of the present invention, utilized at PC ends
LabVIEW softwares gather the colored number of digital camera captured in real-time during asphalt remixer paved mixture in real time
Code image, and use MATLAB softwares to carry out being converted into gray level image, gray level image filtering to the colored digital image collected
Denoising, histogram equalization, gray level image conversion bianry image, little particle filtering, Image erosion, space are filled up, little particle again
Filtering, image segmentation, image expansion processing；Using the asphalt more than 9.5mm particle diameters in the image after processing to gather materials as base
Plinth, the equally distributed computation model of imaged particles is established by the way of asking the side of image four static moment then to try to achieve the coefficient of variation,
Computation model draws evaluation criterion value and evaluation result after calculating image, evaluation result is by wireless network transmissions to remote
Process control central server, by giving PC ends by wireless network Real-time Feedback after remote control center server process, realize
Bituminous mixture laying uniformity is detected in real time, spread out so as to quick, convenient, real-time, quantitative detection asphalt
Uniformity is spread, and the real-time Transmission of data can be realized, ensures the accuracy and promptness of testing result.
Brief description of the drawings
The invention will be further described with reference to the accompanying drawings and examples：
Fig. 1 is the flow chart of the present invention；
Fig. 2 is the calculation diagram of the computation model of the present invention；
Fig. 3 a are the RGB image before colored digital image of the present invention conversion, and Fig. 3 b convert for colored digital image of the present invention
Gray level image afterwards；
Fig. 4 a are the gray level image before present invention filtering, and Fig. 4 b are the filtered gray level image of the present invention；
Fig. 5 a are the gray level image before histogram equalization of the present invention, and Fig. 5 b are the gray scale after histogram equalization of the present invention
Image；
Fig. 6 is the bianry image after the present invention is handled gray level image；
Fig. 7 is the calculation diagram of the short grained filtering of the present invention；
Fig. 8 is that the present invention filters short grained bianry image；
Fig. 9 a are the bianry image before present invention corrosion, and Fig. 9 b are the bianry image after present invention corrosion；
Figure 10 a are the bianry image before space of the present invention is filled up, and Figure 10 b are the bianry image after space of the present invention is filled up；
Figure 11 further filters off short grained bianry image for the present invention；
Figure 12 a are watershed schematic diagram of the present invention, and Figure 12 b are watershed crestal line of the present invention；
Figure 13 a are image before expansive working of the present invention, and Figure 13 b are image after expansive working of the present invention.
Embodiment
As shown in Figure 1：The bituminous mixture laying uniformity real-time detection method of the present embodiment, comprises the following steps：
A. digital camera is gathered in real time in asphalt remixer paved mixture mistake using LabVIEW softwares at PC ends
The colored digital image of captured in real-time in journey；
B. the colored digital image collected is carried out being converted into gray level image, gray-scale map using MATLAB softwares at PC ends
As filtering and noise reduction, histogram equalization, gray level image conversion bianry image, little particle filtering, Image erosion, space fill up, be small
Particle is refiltered, image is split, image expansion is handled；
C. based on the asphalt more than 9.5mm particle diameters to gather materials in the image after processing, using to the side of image four
Ask static moment then to try to achieve the mode of the coefficient of variation and establish the equally distributed computation model of imaged particles, computation model is carried out to image
Evaluation criterion value and evaluation result are drawn after calculating, evaluation result gives remote control center server by wireless network transmissions,
By giving PC ends by wireless network Real-time Feedback after remote control center server process, realize equal to bituminous mixture laying
Even property detects in real time.
In the present embodiment, in step a, by the corresponding IMAQ window of Labview software programmings, and Matlab is utilized
Image procossing script file is combined with Labview softwares, to realize the real-time collection of colored digital image and to collecting
Colored digital image carries out Treatment Analysis.The Matlab image procossing script files of the present embodiment are as shown in table 1.
Table 1Matlab image procossing script files
In order to make the LabVIEW softwares of graphics data stream programming and there is powerful digital image processing techniques function
The advantage of MATLAB softwares is fully played, and applicant is extended with Math Script to MATLAB, the system according to
To hold in the palm in LabVIEW softwares and corresponding control NI-vison, asphalt is gathered in real time when road pavement paves, and
Acquired image is analyzed with MATLAB by means of MATLAB script servers, utilizes flowsheet behind afterwards
During program composition, increase oscillogram and the indicator lamp for representing uniformity results, whole program are circulated using while,
Calculated by 2.5m/min of paver paving operation speed, the time required for the 1m that often paves is 2400ms, therefore image is taken
Sample time interval is set to 2400ms.Programming is carried out by the respective function template provided in vision controls to realize to figure
As being acquired, collection is shown in front panel later, and by 4G wireless networks remote transmission to remote terminal, in remote terminal
When carrying out the analysis calculating of view data and by the image storage of collection to corresponding file, while provide pavement spread
Evaluation for Uniformity standard-coefficient of variation of asphalt, and when finally providing pavement spread asphalt uniformity
Whether conclusion.
In the present embodiment, in step a, digital camera is spread out with the shooting height and illumination captured in real-time asphalt set
Colored digital image of the paving machine during paved mixture, the area for the paved mixture that every colored digital image is gathered
For 720mm × 720mm, the shooting height of the present embodiment is 600mm.
In the present embodiment, in step b, colored digital image is converted into the conversion formula of each pixel in gray level image
For：
Gray=0.299 × R+0.587 × G+0.114 × B, Gray is gray scale in formula, and R is red channel, and G is logical for green
Road, B are blue channel, convert front and rear image as shown in Figure 3 a and Figure 3 b shows.
The gray level image filtering and noise reduction of the present embodiment uses Wiener Filter Method, Wiener filtering (wiener filtering) one
Optimal estimation device of the kind based on minimum mean square error criterion, to stationary process.Between the output of this wave filter and desired output
Mean square error for minimum, therefore, it is an optimum filtering system.It can be used for the letter that extraction is polluted by stationary noise
Number.The details of image can be preferably preserved, so using this wave filter, the image of gray level image filtering and noise reduction before and after the processing is such as
Shown in Fig. 4 a and Fig. 4 b.
The histogram equalization of the present embodiment is in order to strengthen the contrast of image, further, to filtered image
Histogram equalization (as shown in figure 5 a and 5b) is carried out, is found by comparison diagram 5a and Fig. 5 b, is grasped by histogram equalization
Image after work, the contrast of image are remarkably reinforced.
Further, in order to count the area distributions situation of aggregate particle in the plane, it is necessary to convert gray images into two
It is worth image.Method is to be carried out with Otsu methods (maximum between-cluster variance) used by the gray level image conversion bianry image of the present embodiment
Optimal-threshold segmentation, its thought are：Variance is bigger, closer to the threshold value of correct segmentation figure picture.
It is as follows that it tries to achieve optimal threshold k method：
nqIt is the quantity for the pixel that there is gray level to be q, n is the sum of pixel in image.To select suitable threshold value k,
So that maximum between-cluster variance is maximum, following processing is done：
Wherein,For maximum between-cluster variance,If
The k values tried to achieve are not unique, then k values are the average value of multiple k values.After trying to achieve k values, when gray scale just regarding it as less than k
0 pixel of bianry image, gray level just regard its 1 pixel as bianry image more than k's.By Ostu methods to gray-scale map
As the bianry image obtained after being handled is as shown in Figure 6.
The short grained filtering of the present embodiment, for asphalt isolation judge in, influence uniformity mainly
The aggregate particle of greater particle size, simultaneously as the asphalt cutting optimal for AC25 to be paved, therefore, is filtered to uniform
Property influence the small aggregate particle that little particle diameter is less than 9.5mm.Its calculation diagram (as shown in Figure 7), pixel conversion mode is such as
Under：
Make the following assumptions：
(1) aggregate particle is all rounded；
(1) aggregate particle that particle diameter is 9.5mm during actual mixture laying is on the digital image occupied by diameter
Pixel is d,
Obtain d=32.3 ≈ 33；Therefore, whole particle diameter is that the pixel size occupied by 9.5mm circular granular is 850-
Between 900,850 are taken herein.Then the bianry image that will be handled well, short grained filtration treatment of gathering materials is carried out, the figure after processing
As shown in Figure 8.
The etching operation of the image of the present embodiment is to remove inessential fine particle point part in image, is such as schemed
, it is necessary to carry out further etching operation to image shown in 9a, that is, corresponding structural element is selected to do convolution operation, institute to image
The structural element of collection is 3 × 3 rectangular configuration element, and the image through excessive erosion is as shown in figure 9b.
Space is carried out using imfill functions to fill up, enter to image in the present embodiment, in step b, after little particle filtering
After row etching operation find that space occurs inside many aggregate particles, as shown in figures 10 a and 10b, in order in subsequent operation
The area statistics of particle are, it is necessary to these spaces be filled up, so carrying out the closed operation of image and filling up operation；
In view of to after the filling up of space particle, the situation of the increase of particle area, so, progress further to image
It is short grained to dispel, as shown in figure 11.
Further, find there is the particle part of many adhesions in image in processing procedure, as figure 12 a shows, in order to
These particles are further separated, ensure the precision of image, image segmentation is carried out to it, image segmentation uses watershed
The little particle being bonded in image is separated and then filtered off by algorithm, and the area of more than the 9.5mm finally given aggregate particle accounts for
The ratio of whole figure is 40~50 ﹪, so-called watershed algorithm, i other words gray level image, which is understood, turns into a topological surface, table
The size of f (x, y) value is considered as height in face.As figure 12 a shows, watershed transform is i other words find the basin in gray level image
Ground and crestal line, then by it is separated, in the present embodiment mainly using range conversion carry out image watershed segmentation,
The crestal line of segmentation as shown in Figure 12b, the little particle split such as the particle in red circle.
The particle area opened by watershed segmentation is probably the particle that particle diameter is less than 9.5mm, therefore, it is necessary to is further filtered
Go, its computational methods and operating method are referring to short grained filtering.
The image expansion selection of the present embodiment does the structural element of expansive working as 4 × 4 disk templates.After expansion
The image arrived is as shown in Figure 13 a and Figure 13 b.
In the present embodiment, the wireless network uses 4G wireless networks, realizes the real-time Transmission of data.
In the present embodiment, in step c, as shown in Figure 2, it is assumed that aggregate particle is shaped as circle, establishes computation model bag
Include following steps：
C1. the area of each aggregate particle and each aggregate particle distance in bianry image after treatment are counted
The distance on the side of image four, obtains the static moment of aggregate particle area in image to four sides respectively, and its calculation formula is：
In formula：st1(i)、st2(i)、st3(i)、st4(i)、The area of i-th of aggregate particle is represented respectively to pros
Shape image four while (when 1,2 while, 3 while and 4 sides) ask the value of static moment and the average value of each static moment；I-th in s (i) representative images
The area of aggregate particle；l1(i)、l2(i)、l3(i)、l4(i) i-th of side of aggregate particle range image four in bianry image is represented
Distance；
C2. the coefficient of variation of four static moments is obtained by the static moment on four sides and the average value of each static moment, its calculation formula is：
In formula：CvFor the coefficient of variation；
C3. the uniformity that aggregate particle is distributed in image, i.e. coefficient of variation C are weighed with the coefficient of variation obtainedvValue exists
Image between section [0-1.5%], then aggregate particle distribution is more uniform；If image is more than coefficient of variation CvZhi Gai areas
Between, then aggregate particle distributing homogeneity is relatively poor.
Finally illustrate, the above embodiments are merely illustrative of the technical solutions of the present invention and it is unrestricted, although with reference to compared with
The present invention is described in detail good embodiment, it will be understood by those within the art that, can be to the skill of the present invention
Art scheme is modified or equivalent substitution, and without departing from the objective and scope of technical solution of the present invention, it all should cover at this
Among the right of invention.
Claims (7)
- A kind of 1. bituminous mixture laying uniformity real-time detection method, it is characterised in that：Comprise the following steps：A. digital camera is gathered in real time during asphalt remixer paved mixture using LabVIEW softwares at PC ends The colored digital image of captured in real-time；B. MATLAB softwares are used to carry out being converted into gray level image, gray level image filter to the colored digital image collected at PC ends Ripple denoising, histogram equalization, gray level image conversion bianry image, little particle filtering, Image erosion, space are filled up, little particle Refilter, image is split, image expansion processing；C. based on the asphalt more than 9.5mm particle diameters to gather materials in the image after processing, ask quiet using to the side of image four Then mode that square tries to achieve the coefficient of variation establishes the equally distributed computation model of imaged particles, and computation model is calculated image After draw evaluation index value and evaluation result, evaluation result is given remote control center server by wireless network transmissions, passed through PC ends are given by wireless network Real-time Feedback after remote control center server process, are realized to bituminous mixture laying uniformity Detection in real time.
- 2. bituminous mixture laying uniformity real-time detection method according to claim 1, it is characterised in that：In step a, By the corresponding IMAQ window of Labview software programmings, and it is soft using Matlab image procossings script file and Labview Part is combined, to realize that the real-time collection of colored digital image and the colored digital image to collecting carry out Treatment Analysis.
- 3. bituminous mixture laying uniformity real-time detection method according to claim 2, it is characterised in that：In step a, Coloured silk of the digital camera with the shooting height and illumination captured in real-time asphalt remixer that set during paved mixture Color digital image, the area for the paved mixture that every colored digital image is gathered is 720mm × 720mm.
- 4. bituminous mixture laying uniformity real-time detection method according to claim 1, it is characterised in that：In step b, The conversion formula that colored digital image is converted into each pixel in gray level image is：Gray=0.299 × R+0.587 × G+0.114 × B, Gray is gray scale in formula, and R is red channel, and G is green channel, B For blue channel.
- 5. bituminous mixture laying uniformity real-time detection method according to claim 1, it is characterised in that：In step b, It is filled after little particle filtering using imfill function pair little particles, at the same it is small by what is be bonded in image using watershed algorithm Particle is separated and then filtered off, the area of more than the 9.5mm finally given aggregate particle account for the ratio of whole figure for 40~ 50 ﹪.
- 6. bituminous mixture laying uniformity real-time detection method according to claim 1, it is characterised in that：It is described wireless Network uses 4G wireless networks, realizes the real-time Transmission of data.
- 7. bituminous mixture laying uniformity real-time detection method according to claim 1, it is characterised in that：In step c, It is assumed that aggregate particle is shaped as circle, establishes computation model and comprise the following steps：C1. the area of each aggregate particle and each aggregate particle range image in bianry image after treatment are counted The distance on four sides, obtaining aggregate particle area in image respectively, to the static moment on four sides and the average value of each static moment, it calculates public Formula is：<mrow> <mi>s</mi> <mi>t</mi> <mn>1</mn> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mrow> <mo>(</mo> <mi>s</mi> <mo>(</mo> <mi>i</mi> <mo>)</mo> <mo>&times;</mo> <msub> <mi>l</mi> <mn>1</mn> </msub> <mo>(</mo> <mi>i</mi> <mo>)</mo> <mo>)</mo> </mrow> </mrow><mrow> <mi>s</mi> <mi>t</mi> <mn>2</mn> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mrow> <mo>(</mo> <mi>s</mi> <mo>(</mo> <mi>i</mi> <mo>)</mo> <mo>&times;</mo> <msub> <mi>l</mi> <mn>2</mn> </msub> <mo>(</mo> <mi>i</mi> <mo>)</mo> <mo>)</mo> </mrow> </mrow><mrow> <mi>s</mi> <mi>t</mi> <mn>3</mn> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mrow> <mo>(</mo> <mi>s</mi> <mo>(</mo> <mi>i</mi> <mo>)</mo> <mo>&times;</mo> <msub> <mi>l</mi> <mn>3</mn> </msub> <mo>(</mo> <mi>i</mi> <mo>)</mo> <mo>)</mo> </mrow> </mrow> 1<mrow> <mi>s</mi> <mi>t</mi> <mn>4</mn> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mrow> <mo>(</mo> <mi>s</mi> <mo>(</mo> <mi>i</mi> <mo>)</mo> <mo>&times;</mo> <msub> <mi>l</mi> <mn>4</mn> </msub> <mo>(</mo> <mi>i</mi> <mo>)</mo> <mo>)</mo> </mrow> </mrow><mrow> <mover> <mrow> <mi>s</mi> <mi>t</mi> </mrow> <mo>&OverBar;</mo> </mover> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <mi>s</mi> <mi>t</mi> <mn>1</mn> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>s</mi> <mi>t</mi> <mn>2</mn> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>s</mi> <mi>t</mi> <mn>3</mn> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>+</mo> <mi>s</mi> <mi>t</mi> <mn>4</mn> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> <mn>4</mn> </mfrac> </mrow>In formula：st1(i)、st2(i)、st3(i)、st4(i)、The area for representing i-th of aggregate particle respectively aligns square chart As four while (when 1,2 while, 3 while and 4 sides) ask the value of static moment and the average value of each static moment；Gather materials for i-th in s (i) representative images The area of particle；l1(i)、l2(i)、l3(i)、l4(i) represent in bianry image i-th side of aggregate particle range image four away from From；C2. the coefficient of variation of four static moments is obtained by the static moment on four sides and the average value of each static moment, its calculation formula is：<mrow> <msub> <mi>C</mi> <mi>v</mi> </msub> <mo>=</mo> <mfrac> <msqrt> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <munderover> <mi>&Sigma;</mi> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <mn>4</mn> </munderover> <msup> <mrow> <mo>(</mo> <mrow> <mi>s</mi> <mi>t</mi> <mi>j</mi> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>-</mo> <mover> <mrow> <mi>s</mi> <mi>t</mi> </mrow> <mo>&OverBar;</mo> </mover> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>/</mo> <mn>4</mn> </mrow> </msqrt> <mrow> <munderover> <mi>&Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <mover> <mrow> <mi>s</mi> <mi>t</mi> </mrow> <mo>&OverBar;</mo> </mover> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> </mrow> </mfrac> </mrow>In formula：CvFor the coefficient of variation；C3. the uniformity that aggregate particle is distributed in image, i.e. coefficient of variation C are weighed with the coefficient of variation obtainedvValue is in section Image between [0-1.5%], then aggregate particle distribution is more uniform；If image is more than coefficient of variation CvThe section of value, then Aggregate particle distributing homogeneity is relatively poor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710499601.5A CN107341794A (en) | 2017-06-27 | 2017-06-27 | Bituminous mixture laying uniformity real-time detection method |
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| CN108846825A (en) * | 2018-04-24 | 2018-11-20 | 福州大学 | A kind of performance of asphalt mixture evaluation method based on image processing techniques |
| CN110208266A (en) * | 2019-06-20 | 2019-09-06 | 长沙理工大学 | A kind of reclaimed asphalt mixture uniformity evaluating method |
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| CN113433306A (en) * | 2021-07-20 | 2021-09-24 | 重庆交通大学 | Construction evaluation analysis method for loose asphalt pavement |
| CN113433306B (en) * | 2021-07-20 | 2022-01-28 | 重庆交通大学 | Construction evaluation analysis method for loose asphalt pavement |
| DE102022128234A1 (en) | 2021-11-01 | 2023-05-04 | Caterpillar Paving Products Inc. | Paving material segregation detection and monitoring |
| CN117218126A (en) * | 2023-11-09 | 2023-12-12 | 安徽省交通规划设计研究总院股份有限公司 | Asphalt mixture uniformity calculation method in image processing vision field |
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