CN113643316A - New and old aggregate identification method for cold-recycling mixture based on CT image - Google Patents
New and old aggregate identification method for cold-recycling mixture based on CT image Download PDFInfo
- Publication number
- CN113643316A CN113643316A CN202110859364.5A CN202110859364A CN113643316A CN 113643316 A CN113643316 A CN 113643316A CN 202110859364 A CN202110859364 A CN 202110859364A CN 113643316 A CN113643316 A CN 113643316A
- Authority
- CN
- China
- Prior art keywords
- aggregate
- new
- old
- image
- gray
- 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.)
- Granted
Links
- 238000004064 recycling Methods 0.000 title claims abstract description 57
- 239000000203 mixture Substances 0.000 title claims abstract description 47
- 238000000034 method Methods 0.000 title claims abstract description 32
- 239000002245 particle Substances 0.000 claims abstract description 53
- 238000002591 computed tomography Methods 0.000 claims abstract description 36
- 238000012360 testing method Methods 0.000 claims abstract description 27
- 230000011218 segmentation Effects 0.000 claims abstract description 20
- 238000012545 processing Methods 0.000 claims abstract description 16
- 238000004422 calculation algorithm Methods 0.000 claims description 26
- 239000000463 material Substances 0.000 claims description 16
- 239000010426 asphalt Substances 0.000 claims description 10
- 239000004570 mortar (masonry) Substances 0.000 claims description 10
- 238000011049 filling Methods 0.000 claims description 8
- 238000001914 filtration Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 5
- 239000004575 stone Substances 0.000 claims description 5
- 238000003801 milling Methods 0.000 claims description 4
- 230000000877 morphologic effect Effects 0.000 claims description 4
- 230000009467 reduction Effects 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 4
- 239000004927 clay Substances 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 239000000853 adhesive Substances 0.000 claims description 2
- 230000001070 adhesive effect Effects 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 6
- 230000006378 damage Effects 0.000 abstract description 5
- 230000015572 biosynthetic process Effects 0.000 abstract description 3
- 230000008569 process Effects 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000008929 regeneration Effects 0.000 description 2
- 238000011069 regeneration method Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 238000005056 compaction Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- -1 internal gaps Substances 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000005215 recombination Methods 0.000 description 1
- 230000006798 recombination Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/155—Segmentation; Edge detection involving morphological operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/181—Segmentation; Edge detection involving edge growing; involving edge linking
-
- 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
- G06T2207/10072—Tomographic images
- G06T2207/10081—Computed x-ray tomography [CT]
-
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
- G06T2207/20032—Median filtering
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30132—Masonry; Concrete
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
- Image Processing (AREA)
- Processing Of Solid Wastes (AREA)
Abstract
The invention discloses a cold-recycling mixture new and old aggregate identification method based on a CT image, which utilizes industrial CT scanning equipment to obtain CT scanning images of new and old aggregate wrappings and cold-recycling mixture test pieces; processing the CT scanning image by a digital image processing technology to obtain gray distribution characteristic parameters of new and old aggregates and an aggregate section image of the cold-recycling mixture; the method of counting the gray level of aggregate particles firstly and then segmenting the threshold value is utilized to realize the segmentation of the new aggregate and the old aggregate, and the information of the original outline, the size, the shape and the like of the new aggregate and the old aggregate is completely reserved. The method has very important significance for researching the characteristics of the old aggregate particles in the cold-recycling mixture, such as morphology, distribution and the like, and the influence of RAP on the formation, damage and destruction of the cold-recycling mixture skeleton.
Description
Technical Field
The invention belongs to the technical field of highway regeneration mixture tests, and relates to a method for identifying new and old cold regeneration mixture aggregates based on a CT (computed tomography) image.
Background
The recycling of the asphalt pavement reclaimed materials (RAP) has important significance for protecting cultivated land, maintaining ecological environment, reducing pavement construction cost and the like. Compared with the hot recycling technology of the pavement, the cold recycling technology of the asphalt pavement has the characteristics of mixing, construction and compaction at normal temperature, has greater significance for environmental protection and resource saving, can greatly improve the utilization rate of RAP by doping RAP into the base layer, and better realizes the recycling of substances.
The original old aggregate in RAP has different particle properties, surface textures, edge and corner properties and other forms from the new aggregate, and residual asphalt mortar wrapped and adhered on the surface of RAP still has certain viscoelastic properties, so that the RAP shows different mechanical properties from the new aggregate, and a cold-recycling base layer doped with RAP is more complex in the aspects of strength formation and failure mechanism. Therefore, the research on the form, interface and distribution characteristics of the old aggregate particles in the cold-recycling mixture and the relationship between the old aggregate particles and the crack evolution behavior is necessary for disclosing the damage mechanism of the cold-recycling mixture.
The CT image technology can be used for identifying three materials, namely internal gaps, mortar and aggregate according to the density difference of the materials formed by the cold-recycling mixture, and an aggregate section image is extracted more completely. However, the gray distribution of the interior and the edge of the aggregate image particles is not uniform, and the segmentation of the aggregate by only using a single threshold segmentation method can cause the regions above the threshold in the aggregate to be reserved and the regions below the threshold to be separated, so that more defects and holes can be generated in the interior of the new and old aggregate particles after segmentation, and the edges can have contour residues.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for identifying new and old aggregates of cold-recycling mixture based on a CT image. The method has very important significance for researching the characteristics of the old aggregate particles in the cold-recycling mixture, such as morphology, distribution and the like, and the influence of RAP on the formation, damage and destruction of the cold-recycling mixture skeleton.
The purpose of the invention is realized by the following technical scheme:
a cold-recycling mixture new and old aggregate identification method based on CT images comprises the following steps:
the method comprises the following steps: preparing new and old aggregate wrappings and cold-recycling mixture test pieces, and respectively scanning the new and old aggregate wrappings and the cold-recycling mixture test pieces by adopting industrial CT scanning equipment to obtain CT section images of the new and old aggregate wrappings;
step two: counting the gray distribution of the CT section image of the new and old aggregate wrappings obtained in the first step, and acquiring gray values corresponding to the peak values of the new and old aggregates and gray distribution interval information from a gray histogram so as to calculate the gray distribution characteristic parameters of the new and old aggregates;
step three: preprocessing a cold-recycling mixture test piece CT image by adopting a filtering noise reduction and gray level transformation enhancement algorithm, and performing threshold segmentation processing on the cold-recycling mixture test piece CT image by utilizing an annular Otsu algorithm to preliminarily obtain an aggregate section image;
step four: processing mortar residues at the edges of the aggregate particles, internal holes and adhesive mortar between the aggregates in the aggregate section image obtained in the step three by adopting an image opening operation, an image closing operation, hole filling and watershed algorithm to obtain an aggregate section image capable of reflecting the information of the real outline, size and quantity of the aggregates;
step five: counting the gray distribution of the aggregate particles of the aggregate section image processed in the step four to obtain a gray value M corresponding to the peak value of the gray histogram of the aggregate section imagedAccording to MdCalculating the gray level threshold value K of the new and old aggregates of the aggregate section image according to the corresponding aggregate types and the gray level distribution characteristic parameters of the new and old aggregates obtained in the step two;
step six: marking all aggregate particles in the aggregate section image processed in the fourth step by using an image marking algorithm, extracting each aggregate particle based on the aggregate mark, counting the gray value distribution of pixel points in each aggregate particle, and obtaining the gray value R corresponding to the peak value of each aggregate particle from the gray histogram(i)；
Step seven: the gray value R corresponding to each aggregate particle peak value obtained in the step six(i)Comparing with the new and old aggregate gray threshold K obtained in the step five, if R is(i)If greater than K, the aggregate particles are identified as old aggregate, if R(i)And if the K is less than the K, the aggregate particles are identified as new aggregates, and all the identified new aggregates and all the identified old aggregates are recombined to obtain a complete new aggregate and old aggregate extraction image.
Compared with the prior art, the invention has the following advantages:
the invention provides a method for acquiring mesoscopic graphic information of new and old aggregates in a cold-recycling mixture based on a CT (computed tomography) technology aiming at the phenomenon that the new and old aggregates in the cold-recycling mixture have density difference at present, greatly improves the precision of segmenting gray level images of substances with uneven density, and provides a new means for researching the distribution and the mesoscopic characteristics of the new and old aggregates in the cold-recycling mixture of an asphalt pavement.
Drawings
FIG. 1 is an original cross-sectional image of new and old aggregate wraps and their CT scan;
FIG. 2 is a cold-recycling mixed material test piece and a CT scanning original section image thereof;
FIG. 3 is a schematic diagram of the gray distribution of CT images of new and old aggregate wrappings;
FIG. 4 is a schematic view of a cold-recycling mixture aggregate section after being segmented by an annular Otsu algorithm;
FIG. 5 is a schematic view of an aggregate section after being processed by a morphological watershed algorithm;
FIG. 6 is a schematic diagram showing the gray level distribution of the aggregate particles in the aggregate section image;
FIG. 7 is a schematic illustration of aggregate marking and extraction;
FIG. 8 is a schematic illustration of the extracted individual aggregate particle gray scale distribution;
fig. 9 is a schematic view of new and old aggregates after recombination of the aggregate particles.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings, but not limited thereto, and any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention shall be covered by the protection scope of the present invention.
The invention provides a cold-recycling mixture new and old aggregate identification method based on a CT image, which utilizes industrial CT scanning equipment to obtain CT scanning images of new and old aggregate wrappings and cold-recycling mixture test pieces; processing the CT scanning image by a digital image processing technology to obtain gray distribution characteristic parameters of new and old aggregates and an aggregate section image of the cold-recycling mixture; the method of counting the gray level of aggregate particles firstly and then segmenting the threshold value is utilized to realize the segmentation of the new aggregate and the old aggregate, and the information of the original outline, the size, the shape and the like of the new aggregate and the old aggregate is completely reserved. The method specifically comprises the following steps:
the method comprises the following steps: preparing new and old aggregate wrappings and cold-recycling mixture test pieces, and respectively scanning the new and old aggregate wrappings and the cold-recycling mixture test pieces by adopting industrial CT scanning equipment to obtain CT section images of the new and old aggregate wrappings.
In the step, the old aggregate of the cold-recycling mixture test piece comes from the asphalt pavement milling material, and the new aggregate comes from the base crushed stone; the new and old aggregate wrappage is prepared by wrapping base crushed stones and the asphalt pavement milling material by light clay.
Step two: and (4) counting the gray distribution of the CT section image of the new and old aggregate wrappings obtained in the first step, and obtaining gray values corresponding to the peak values of the new and old aggregates and gray distribution interval information from a gray histogram so as to calculate the gray distribution characteristic parameters of the new and old aggregates.
In this step, the gray distribution characteristic parameters include the gray value difference G between the new and old aggregate peak valuesdInterval length L between new and old aggregate gray distribution intervalNAnd LRWherein G isdIs the difference between the gray scale value corresponding to the peak value of the old aggregate and the gray scale value corresponding to the peak value of the new aggregate in the gray scale histogram, LNAnd LRThe difference between the upper gray scale limit and the lower gray scale limit of the new and old aggregate gray scale distribution intervals is respectively.
Step three: and preprocessing the CT image of the cold-recycling mixture test piece by adopting a filtering noise reduction and gray level transformation enhancement algorithm, and performing threshold segmentation processing on the CT image of the cold-recycling mixture test piece by utilizing an annular Otsu algorithm to preliminarily obtain an aggregate section image.
In the step, the annular Otsu algorithm is used for processing the problem of light and shade difference generated at positions with different distances from the central axis of the test piece, namely, the test piece is divided into rings and then threshold segmentation is carried out on each region by using the Otsu algorithm; and (3) performing annular Otsu threshold segmentation twice on the original cold-recycling mixture test piece CT image, and reserving a part higher than the threshold after each segmentation so as to preliminarily obtain an aggregate section image.
Step four: and (4) processing mortar residues at the edges of the aggregate particles, internal holes and bonding mortar between the aggregate and the aggregate in the aggregate section image obtained in the step three by adopting an image opening operation, an image closing operation, hole filling and watershed algorithm to obtain an aggregate section image capable of reflecting the information of the real outline, size and quantity of the aggregate.
In the step, the selection of the parameter value needs to be manually adjusted according to the actual image; selecting a disc array matrix with the radius of 3 pixel points as a morphological image processing basic structural element, and filling small gaps in the aggregate through image closing operation; secondly, filtering out damaged parts relative to the basic structural elements through image opening operation, namely redundant mortar particles; preprocessing is carried out on the watershed algorithm segmentation again through a hole filling algorithm, closed gaps in the aggregate are filled, and the aggregate is prevented from being regarded as a catchment basin; and finally, segmenting the bonded aggregate particles by a watershed algorithm to obtain an aggregate section image capable of reflecting the information of the real aggregate contour, size and quantity.
Step five: counting the gray distribution of the aggregate particles of the aggregate section image processed in the step four to obtain a gray value M corresponding to the peak value of the gray histogram of the aggregate section imagedAccording to MdAnd (4) calculating the gray level threshold value K of the new and old aggregates of the aggregate section image according to the corresponding aggregate types and the gray level distribution characteristic parameters of the new and old aggregates obtained in the step two.
In this step, the new and old aggregate ashesThe calculation formula of the degree threshold value K is as follows: k is Md+Ld+LS+k；
When M isdCorresponding to the gray peak of the new aggregate, Ld＝Gd，LS＝-LR/2；
When M isdCorresponding to the gray peak of the old aggregated＝-Gd，LS＝LN/2；
k is a brightness adjusting coefficient, k belongs to [ -5, 5], and the k value is adjusted artificially according to the brightness difference of the aggregate image.
Step six: marking all aggregate particles in the aggregate section image processed in the fourth step by using an image marking algorithm, extracting each aggregate particle based on the aggregate mark, counting the gray value distribution of pixel points in each aggregate particle, and obtaining the gray value R corresponding to the peak value of each aggregate particle from the gray histogram(i)。
Step seven: the gray value R corresponding to each aggregate particle peak value obtained in the step six(i)Comparing with the new and old aggregate gray threshold K obtained in the step five, if R is(i)If greater than K, the aggregate particles are identified as old aggregate, if R(i)And if the K is less than the K, the aggregate particles are identified as new aggregates, and all the identified new aggregates and all the identified old aggregates are recombined to obtain a complete new aggregate and old aggregate extraction image.
Example (b):
the embodiment provides a method for identifying new and old aggregates of cold-recycling mixture based on CT images, which comprises the following specific operation processes:
the method comprises the following steps: wrapping base crushed stone and asphalt pavement milling material by adopting light clay to prepare a new and old aggregate wrap, and forming a cement stable cold-recycling mixture test piece (50% of RAP doping amount) by a static pressure/vibration forming method; and then respectively scanning the new and old aggregate wrappings and the cold-recycling mixed material test piece by using industrial CT, wherein the voltage and current adopted by X rays in the test process are respectively 190kv and 110 muA, the scanning fault spacing is 0.1mm, the image derivation is 256-level gray image, and the internal section image of the test piece is obtained as shown in figures 1 and 2.
Step two: counting the gray distribution of the CT section image of each new and old aggregate wrap obtained in the first step, obtaining the gray values corresponding to the peak values of the new and old aggregates and the gray distribution interval information of the new and old aggregates from the gray histogram, calculating the gray distribution characteristic parameters of the new and old aggregates, and obtaining the gray value difference G of the peak values of the new and old aggregatesdAnd the interval length L of the gray level distribution of the new and old aggregatesNAnd LRAs shown in fig. 3.
Step three: preprocessing a CT image of the cold-recycling mixed material test piece by adopting a median filtering noise reduction and gray level transformation enhancement algorithm, performing threshold segmentation processing on the CT image of the cold-recycling mixed material test piece by utilizing an annular Otsu algorithm, performing annular Otsu threshold segmentation on the CT image of the original cold-recycling mixed material test piece twice, and retaining a part higher than a threshold after each segmentation so as to preliminarily obtain an aggregate section image (as shown in figure 4).
Step four: selecting a disc array matrix with the radius of 3 pixel points as a morphological image processing basic structural element, processing the aggregate section image obtained in the step three, and filling fine gaps in the aggregate through image closing operation; secondly, filtering out damaged parts relative to the basic structural elements through image opening operation, namely redundant mortar particles; preprocessing is carried out on the watershed algorithm segmentation again through a hole filling algorithm, closed gaps in the aggregate are filled, and the aggregate is prevented from being regarded as a catchment basin; finally, the bonded aggregate particles are segmented by a watershed algorithm to obtain an aggregate section image (as shown in figure 5) capable of reflecting the information of the real aggregate contour, size and quantity.
Step five: counting the gray distribution of the aggregate particles of the aggregate section image processed in the step four to obtain a gray value M corresponding to the peak value of the gray histogram of the aggregate section imaged(as shown in fig. 6), it can be determined that the gray scale peak belongs to the new aggregate peak according to the gray scale interval where the gray scale peak is located, and then the gray scale threshold K, K being M, of the new and old aggregates of the aggregate section image is calculatedd+Ld+LS+k。
Step six: utilizing an image marking algorithm to process the aggregate section image processed in the fourth stepMarking all the aggregate particles, extracting single aggregate particles based on the aggregate marks (as shown in fig. 7), counting the gray value distribution of the internal pixel points of each aggregate particle, and obtaining the gray value R corresponding to the peak value of each aggregate particle from the gray histogram(i)(as shown in fig. 8).
Step seven: the gray value R corresponding to each aggregate particle peak value obtained in the step six(i)Comparing with the new and old aggregate gray threshold K obtained in the step five, if R is(i)If greater than K, the aggregate particles are identified as old aggregate, if R(i)And if the K is less than the K, the aggregate particles are identified as new aggregates, and all the identified new and old aggregates are recombined to obtain a complete new and old aggregate extraction image (as shown in figure 9).
In conclusion, the new and old aggregates extracted by the new and old aggregate identification method can completely retain the aggregate outline, and simultaneously eliminate the interference of uneven density points in the aggregates, so that the integral integrity of the aggregate particles is preserved. The method for carrying out particle gray scale statistics and threshold segmentation greatly improves the precision of segmentation of gray scale images of substances with uneven density, and provides a new means for researching the distribution and the microscopic characteristics of new and old aggregates in the cold-recycling mixture of the asphalt pavement.
Claims (7)
1. A cold-recycling mixture new and old aggregate identification method based on CT images is characterized by comprising the following steps:
the method comprises the following steps: preparing new and old aggregate wrappings and cold-recycling mixture test pieces, and respectively scanning the new and old aggregate wrappings and the cold-recycling mixture test pieces by adopting industrial CT scanning equipment to obtain CT section images of the new and old aggregate wrappings;
step two: counting the gray distribution of the CT section image of the new and old aggregate wrappings obtained in the first step, and acquiring gray values corresponding to the peak values of the new and old aggregates and gray distribution interval information from a gray histogram so as to calculate the gray distribution characteristic parameters of the new and old aggregates;
step three: preprocessing a cold-recycling mixture test piece CT image by adopting a filtering noise reduction and gray level transformation enhancement algorithm, and performing threshold segmentation processing on the cold-recycling mixture test piece CT image by utilizing an annular Otsu algorithm to preliminarily obtain an aggregate section image;
step four: processing mortar residues at the edges of the aggregate particles, internal holes and adhesive mortar between the aggregates in the aggregate section image obtained in the step three by adopting an image opening operation, an image closing operation, hole filling and watershed algorithm to obtain an aggregate section image capable of reflecting the information of the real outline, size and quantity of the aggregates;
step five: counting the gray distribution of the aggregate particles of the aggregate section image processed in the step four to obtain a gray value M corresponding to the peak value of the gray histogram of the aggregate section imagedAccording to MdCalculating the gray level threshold value K of the new and old aggregates of the aggregate section image according to the corresponding aggregate types and the gray level distribution characteristic parameters of the new and old aggregates obtained in the step two;
step six: marking all aggregate particles in the aggregate section image processed in the fourth step by using an image marking algorithm, extracting each aggregate particle based on the aggregate mark, counting the gray value distribution of pixel points in each aggregate particle, and obtaining the gray value R corresponding to the peak value of each aggregate particle from the gray histogram(i)；
Step seven: the gray value R corresponding to each aggregate particle peak value obtained in the step six(i)Comparing with the new and old aggregate gray threshold K obtained in the step five, if R is(i)If greater than K, the aggregate particles are identified as old aggregate, if R(i)And if the K is less than the K, the aggregate particles are identified as new aggregates, and all the identified new aggregates and all the identified old aggregates are recombined to obtain a complete new aggregate and old aggregate extraction image.
2. The method for identifying new and old cold-recycling mixed material based on CT images as claimed in claim 1, wherein in the first step, the old aggregate of the cold-recycling mixed material specimen is from asphalt pavement milling material, and the new aggregate is from base crushed stone.
3. The method for identifying new and old aggregates of cold-recycling mix based on CT image as claimed in claim 1, wherein in the first step, the new and old aggregates are wrapped with light clay to prepare a wrapping material for wrapping the crushed stones of the base course and the milled material of the asphalt pavement.
4. The method for identifying new and old aggregates in cold-recycling mix based on CT image as claimed in claim 1, wherein in the second step, the gray distribution characteristic parameters include the peak gray value difference G between new and old aggregatesdInterval length L between new and old aggregate gray distribution intervalNAnd LRWherein G isdIs the difference between the gray scale value corresponding to the peak value of the old aggregate and the gray scale value corresponding to the peak value of the new aggregate in the gray scale histogram, LNAnd LRThe difference between the upper gray scale limit and the lower gray scale limit of the new and old aggregate gray scale distribution intervals is respectively.
5. The method for identifying the new and old aggregates of the cold-recycling mixture based on the CT image as claimed in claim 1, wherein in the third step, the CT image of the original cold-recycling mixture specimen is subjected to two annular Otsu threshold value segmentations, and the part higher than the threshold value is remained after each segmentation, so that an aggregate section image can be obtained preliminarily.
6. The method for identifying new and old aggregates in cold-recycling mix based on CT image as claimed in claim 1, wherein in the fourth step, a disc array matrix with radius of 3 pixel points is selected as a morphological image processing basic structural element, and the aggregate image is processed in sequence according to the sequence of image closing operation, image opening operation, hole filling and watershed algorithm.
7. The method for identifying the new and old aggregates of the cold-recycling mix based on the CT image as recited in claim 1, wherein in the fifth step, the gray level threshold K of the new and old aggregates is calculated according to the following formula:
K＝Md+Ld+LS+k；
when M isdCorresponding to the gray peak of the new aggregate, Ld＝Gd，LS＝-LR/2；
When M isdCorresponding to the gray peak of the old aggregated＝-Gd，LS＝LN/2；
k is a luminance adjustment coefficient, and k is ∈ [ -5, 5 ].
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110859364.5A CN113643316B (en) | 2021-07-28 | 2021-07-28 | New and old aggregate identification method for cold-recycling mixture based on CT image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110859364.5A CN113643316B (en) | 2021-07-28 | 2021-07-28 | New and old aggregate identification method for cold-recycling mixture based on CT image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113643316A true CN113643316A (en) | 2021-11-12 |
| CN113643316B CN113643316B (en) | 2022-03-11 |
Family
ID=78418714
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110859364.5A Active CN113643316B (en) | 2021-07-28 | 2021-07-28 | New and old aggregate identification method for cold-recycling mixture based on CT image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113643316B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114463240A (en) * | 2022-02-10 | 2022-05-10 | 哈尔滨工业大学 | Fusion processing method of neutron image and X-ray image in asphalt mixture |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106447690A (en) * | 2016-09-30 | 2017-02-22 | 南京航空航天大学 | Coarse aggregation characteristic method for emulsified asphalt cold-recycled mixture |
| CN106651896A (en) * | 2016-09-30 | 2017-05-10 | 南京航空航天大学 | Material classification method of CT images of asphalt mixture |
| US20180142428A1 (en) * | 2016-11-22 | 2018-05-24 | Green Asphalt Co. Llc | Recycled asphalt production apparatus |
| CN108734699A (en) * | 2018-04-23 | 2018-11-02 | 东南大学 | A method of evaluation reclaimed asphalt mixture uniform component distribution |
| CN111553886A (en) * | 2020-04-09 | 2020-08-18 | 哈尔滨工业大学 | Self-adaptive identification method for particles in asphalt mixture CT image |
-
2021
- 2021-07-28 CN CN202110859364.5A patent/CN113643316B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106447690A (en) * | 2016-09-30 | 2017-02-22 | 南京航空航天大学 | Coarse aggregation characteristic method for emulsified asphalt cold-recycled mixture |
| CN106651896A (en) * | 2016-09-30 | 2017-05-10 | 南京航空航天大学 | Material classification method of CT images of asphalt mixture |
| US20180142428A1 (en) * | 2016-11-22 | 2018-05-24 | Green Asphalt Co. Llc | Recycled asphalt production apparatus |
| CN108734699A (en) * | 2018-04-23 | 2018-11-02 | 东南大学 | A method of evaluation reclaimed asphalt mixture uniform component distribution |
| CN111553886A (en) * | 2020-04-09 | 2020-08-18 | 哈尔滨工业大学 | Self-adaptive identification method for particles in asphalt mixture CT image |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114463240A (en) * | 2022-02-10 | 2022-05-10 | 哈尔滨工业大学 | Fusion processing method of neutron image and X-ray image in asphalt mixture |
| CN114463240B (en) * | 2022-02-10 | 2022-11-18 | 哈尔滨工业大学 | Fusion processing method of neutron image and X-ray image in asphalt mixture |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113643316B (en) | 2022-03-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Homogeneity evaluation of hot in-place recycling asphalt mixture using digital image processing technique | |
| Puppala et al. | Volumetric shrinkage strain measurements in expansive soils using digital imaging technology | |
| CN113643316B (en) | New and old aggregate identification method for cold-recycling mixture based on CT image | |
| CN110208266B (en) | Method for evaluating uniformity of recycled asphalt mixture | |
| CN112561903B (en) | Temperature shrinkage crack resistance method suitable for asphalt pavement in cold region | |
| CN110956183B (en) | Asphalt pavement crack morphology extraction method | |
| CN113203743B (en) | Roadbed crack detection, identification and repair method based on infrared thermal imaging analysis | |
| CN111626529B (en) | Method for evaluating RAP dispersion degree in reclaimed asphalt mixture | |
| CN105654482B (en) | Based on the determination method of the soil-rock mixture characterization unit body scale of Digital image technology | |
| CN110398444B (en) | Form detection and grading estimation method for cold aggregate particle system in asphalt pavement construction process based on movable sliding block | |
| Li et al. | Effect of aggregate properties on long-term skid resistance of asphalt mixture | |
| CN117635582B (en) | Image analysis-based asphalt preventive maintenance agent coating rate evaluation method | |
| Peterson et al. | Methods for threshold optimization for images collected from contrast enhanced concrete surfaces for air-void system characterization | |
| JP2011106923A (en) | Particle size specifying method and particle size specifying system for granular material | |
| CN117408928B (en) | Method for determining permeable concrete pavement structure based on image processing | |
| Castillo et al. | Image-based gradation and aggregate characterisation: case of cement-stabilised quarry fines | |
| Tielmann et al. | Air void analyses on asphalt specimens using plane section preparation and image analysis | |
| CN108797255A (en) | A kind of asphalt pavement crack homogeneity restorative procedure | |
| CN108288266A (en) | A kind of regenerative bone material by concrete corner loss late and mortar peel off the method for quantitatively evaluating of area | |
| CN114441530A (en) | Method for evaluating activation degree of old asphalt mixture of pavement | |
| Alshibli et al. | Quantifying void ratio in granular materials using Voronoi tessellation | |
| Zhu et al. | Effect of Clogging and Cleaning on the Air Void Microstructure of Porous Asphalt Concrete | |
| Rolt et al. | Back analysis of previous constructed low volume rural roads in Mozambique | |
| CN114511541B (en) | Three-dimensional digital collection library rapid establishing and evaluating method based on X-ray CT | |
| Druta et al. | Laboratory investigation of reclaimed asphalt pavement mixed with pure binder using X-ray CT scanner |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |