CN109544624A - A kind of rock fragmentation image analysis system - Google Patents
A kind of rock fragmentation image analysis system Download PDFInfo
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- CN109544624A CN109544624A CN201811329236.4A CN201811329236A CN109544624A CN 109544624 A CN109544624 A CN 109544624A CN 201811329236 A CN201811329236 A CN 201811329236A CN 109544624 A CN109544624 A CN 109544624A
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- 239000011435 rock Substances 0.000 title claims abstract description 47
- 238000013467 fragmentation Methods 0.000 title claims abstract description 44
- 238000006062 fragmentation reaction Methods 0.000 title claims abstract description 44
- 238000010191 image analysis Methods 0.000 title claims abstract description 13
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 12
- 238000001914 filtration Methods 0.000 claims abstract description 4
- 230000010354 integration Effects 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 6
- 230000000877 morphologic effect Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 abstract description 7
- 230000011218 segmentation Effects 0.000 abstract description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000003708 edge detection Methods 0.000 description 2
- 238000003709 image segmentation Methods 0.000 description 2
- 238000007654 immersion Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000003703 image analysis method Methods 0.000 description 1
- 229910052500 inorganic mineral Inorganic materials 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 239000011707 mineral Substances 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/28—Measuring arrangements characterised by the use of optical techniques for measuring areas
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-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
- G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
-
- 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
- G06T2207/10004—Still image; Photographic image
-
- 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
- G06T2207/10024—Color image
-
- 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
- G06T2207/20036—Morphological image processing
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- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a kind of rock fragmentation image analysis system, method and step includes: the color RGB image for obtaining rock fragmentation, and color RGB image is converted to gray level image；Fractional order integration smothing filtering is carried out to gray level image, obtains smoothed out image；Boundary scan is carried out to smoothed out image, and processing is split to result images, obtains label image, includes multiple target lumpiness in label image；Obtain the area of each target lumpiness in the target label image；The area of all target lumpiness is counted, the distribution of rock fragmentation size is obtained.The present invention obtains the area of each target lumpiness in target label image using watershed algorithm, the area of all target lumpiness is counted, obtain the distribution of rock fragmentation size, improve rock fragmentation segmentation problem in image processing process, it can be good at the phenomenon that solving rock fragmentation poly- heap, rock fragmentation real-time measurement may be implemented, save human cost, significantly improve economic benefit.
Description
Technical field
The present invention relates to engineering explosion technical field, in particular to a kind of rock fragmentation image analysis system.
Background technique
In mineral engineering and hydraulic and hydroelectric engineering, the measurement of the size distribution of rock fragmentation is very important.It is good
Good LUMPINESS DISTRIBUTION can be very good to save broken cost, and in hydraulic and hydroelectric engineering, the formation on heap dam is distributed rock fragmentation
Also there is critically important requirement.The method of rock fragmentation distribution measuring mainly has the direct method of measurement and the indirect method of measurement, directly measures
Method is exactly to utilize sieve method, and this method takes a long time, and needs very big artificial investment, and economic benefit is poor.It surveys indirectly
Image analysis method is a kind of simple and fast analysis method in amount method, but is needed in image segmentation there are some problems
Further amendment.
The technology that rock fragmentation is analyzed currently with image procossing gradually grows up, patent
CN201710519762.6 mentions a kind of rock fragmentation image measuring method, is changed to rock fragmentation edge detection algorithm
Into.Patent CN201610097313.2 proposes gradually layering and reduces the poly- heap rock adhesion stone degree separation algorithm of thought to carry out
LUMPINESS DISTRIBUTION image procossing.But these measurement methods measure rock fragmentation, the investment of manpower needs to improve.
Summary of the invention
In order to solve the existing problems, the purpose of the present invention is to provide a kind of rock fragmentation image analysis systems, utilize
Watershed algorithm improves rock fragmentation segmentation problem in image processing process, can be good at solving showing for the poly- heap of rock fragmentation
As rock fragmentation real-time measurement may be implemented, save human cost, economic benefit is significantly improved, to solve above-mentioned background technique
The problem of middle proposition.
To achieve the above object, the invention provides the following technical scheme:
A kind of rock fragmentation image analysis system, method include the following steps:
Step 1: obtaining the color RGB image of rock fragmentation, and the color RGB image is converted to gray level image:
Step 2: fractional order integration smothing filtering is carried out to the gray level image, obtains smoothed out image:
Step 3: boundary scan is carried out to the smoothed out image, and processing is split to the result images, is obtained
To label image；It include multiple target lumpiness in the label image；
Step 4: the area of each target lumpiness in the target label image is obtained；
Step 5: counting the area of all target lumpiness, obtains the distribution of rock fragmentation size.
Preferably, processing is split to image using the watershed algorithm based on morphological operations in step 3.
Preferably, a width gray level image is obtained by step 2, minimum and maximum gray value is h_max and h_min.
Preferably, lumpiness Drawing of Curve process is as follows in step 4:
Firstly, the area to sillar is ranked up from small to large
S [n]=sort (s) (1)
Secondly, i element before the sillar area matrix s [n] after sequence is added up to obtain new matrix as [n],
In
As [i] indicates that sillar area is less than or equal to the sum of all sillar areas of s [i].
Compared with prior art, the beneficial effects of the present invention are: rock fragmentation image analysis system proposed by the present invention, is incited somebody to action
The introducing of watershed algorithm obtains the area of each target lumpiness in target label image using watershed algorithm, to all mesh
The area of mark lumpiness is counted, and is ranked up from small to large to the area of sillar, is obtained the distribution of rock fragmentation size, is improved
Rock fragmentation segmentation problem in image processing process can be good at the phenomenon that solving rock fragmentation poly- heap, rock may be implemented
Lumpiness real-time measurement saves human cost, significantly improves economic benefit.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Referring to Fig. 1, a kind of rock fragmentation image analysis system, method include the following steps:
Step 1: obtaining the color RGB image of rock fragmentation, and color RGB image is converted to gray level image:
Step 2: fractional order integration smothing filtering is carried out to gray level image, obtains smoothed out image:
Step 3: boundary scan is carried out to smoothed out image, and processing is split to result images, obtains labeled graph
Picture；It include multiple target lumpiness in label image, then in this step, using the watershed algorithm based on morphological operations to image
It is split processing, watershed segmentation methods are a kind of dividing methods of mathematical morphology based on topological theory, think substantially
Want image to be regarded as the topological landforms in geodesy, the gray value of every bit pixel indicates the height above sea level of the point in image
Degree, each local minimum and its influence area are known as reception basin, and the boundary of reception basin then forms watershed.Watershed
Concept and formation can be illustrated by simulation immersion process.On each local minimum surface, an aperture is pierced through, then
Entire model is slowly immersed in the water, with the intensification of immersion, the domain of influence of each local minimum is slowly extended to the outside,
Two reception basin meets construct dam, that is, form watershed；
Step 4: the area of each target lumpiness in target label image is obtained；
Step 5: counting the area of all target lumpiness, obtains the distribution of rock fragmentation size.
A width gray level image is obtained by step 2, its minimum and maximum gray value is h_max and h_min.Define one
A water level h from h_min to h_max constantly incremental recursive procedure.In this process each from different Local Minimum phases
The catchment basin of pass all constantly extends, and defines the union of sets that X (h) is denoted as the catchment basin in water level h.At h+1 layers, one
The expansion in new Local Minimum of connected component T (h+1) or a basin in an already existing X (h)
Exhibition.For the latter, put at a distance from each catchment basin by each that syntople computed altitude is h+1.If point with
More than two basins are equidistant, then it is not belonging to any basin, and otherwise it belongs to it apart from nearest basin.In this way to
Generate new X (h+1).The Local Minimum occurred in height h is denoted as MIN (h).It is that h+1 is same that Y (h+1, X (h)), which is denoted as height,
When belong to X (h) point set.
Watershed transform Watershed (f) is exactly the supplementary set of X (h_max):
Watershed (f)=D (h_max)
Watershed transform due to it is intuitive, quickly and can always generate complete boundary with parallel computation, in this way it is avoided that
The post-processing of contour connection.
Lumpiness Drawing of Curve process is as follows in step 4:
Firstly, the area to sillar is ranked up from small to large
S [n]=sort (s) (1)
Secondly, i element before the sillar area matrix s [n] after sequence is added up to obtain new matrix as [n],
In
As [i] indicates that sillar area is less than or equal to the sum of all sillar areas of s [i].
The present invention successively passes through image taking, image grayscale conversion, Image Edge-Detection, image segmentation, cut zone face
Product calculating, area accumulation and LUMPINESS DISTRIBUTION curve and etc., each mesh in target label image is obtained using watershed algorithm
The area for marking lumpiness, improves rock fragmentation segmentation problem in image processing process.It can be good at solving the poly- heap of rock fragmentation
The phenomenon that.
In conclusion rock fragmentation image analysis system proposed by the present invention, by the introducing of watershed algorithm, using dividing water
Ridge algorithm obtains the area of each target lumpiness in target label image, counts to the area of all target lumpiness, to rock
The area of block is ranked up from small to large, obtains the distribution of rock fragmentation size, improves rock fragmentation point in image processing process
Cut problem, can be good at the phenomenon that solving rock fragmentation poly- heap, rock fragmentation real-time measurement may be implemented, save manpower at
This, significantly improves economic benefit.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (4)
1. a kind of rock fragmentation image analysis system, which is characterized in that method includes the following steps:
Step 1: obtaining the color RGB image of rock fragmentation, and the color RGB image is converted to gray level image:
Step 2: fractional order integration smothing filtering is carried out to the gray level image, obtains smoothed out image:
Step 3: boundary scan is carried out to the smoothed out image, and processing is split to the result images, is marked
Number image；It include multiple target lumpiness in the label image；
Step 4: the area of each target lumpiness in the target label image is obtained；
Step 5: counting the area of all target lumpiness, obtains the distribution of rock fragmentation size.
2. a kind of rock fragmentation image analysis system as described in claim 1, which is characterized in that use be based in step 3
The watershed algorithm of morphological operations is split processing to image.
3. a kind of rock fragmentation image analysis system as described in claim 1, which is characterized in that obtain a width by step 2
Gray level image, minimum and maximum gray value are h_max and h_min.
4. a kind of rock fragmentation image analysis system as described in claim 1, which is characterized in that lumpiness curve is drawn in step 4
Process processed is as follows:
Firstly, the area to sillar is ranked up from small to large
S [n]=sort (s) (1)
Secondly, i element before the sillar area matrix s [n] after sequence is added up to obtain new matrix as [n], wherein
As [i] indicates that sillar area is less than or equal to the sum of all sillar areas of s [i].
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811329236.4A CN109544624A (en) | 2018-11-09 | 2018-11-09 | A kind of rock fragmentation image analysis system |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201811329236.4A CN109544624A (en) | 2018-11-09 | 2018-11-09 | A kind of rock fragmentation image analysis system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113177949A (en) * | 2021-04-16 | 2021-07-27 | 中南大学 | Large-size rock particle feature identification method and device |
| CN113344945A (en) * | 2021-05-31 | 2021-09-03 | 沈阳工业大学 | Rock mass blasting blockiness automatic analysis device and method based on binocular vision |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030156739A1 (en) * | 2002-02-15 | 2003-08-21 | Inco Limited | Rock fragmentation analysis system |
| WO2012118868A2 (en) * | 2011-02-28 | 2012-09-07 | Schlumberger Technology Corporation | Petrographic image analysis for determining capillary pressure in porous media |
| CN107314957A (en) * | 2017-06-30 | 2017-11-03 | 长安大学 | A kind of measuring method of rock fragmentation Size Distribution |
| CN108550154A (en) * | 2018-04-11 | 2018-09-18 | 中国科学院西双版纳热带植物园 | A kind of method of accurately measuring karst earth's surface bare rock accounting |
| WO2018201180A1 (en) * | 2017-05-02 | 2018-11-08 | PETRA Data Science Pty Ltd | Automated, real time processing, analysis, mapping and reporting of data for the detection of geotechnical features |
-
2018
- 2018-11-09 CN CN201811329236.4A patent/CN109544624A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030156739A1 (en) * | 2002-02-15 | 2003-08-21 | Inco Limited | Rock fragmentation analysis system |
| WO2012118868A2 (en) * | 2011-02-28 | 2012-09-07 | Schlumberger Technology Corporation | Petrographic image analysis for determining capillary pressure in porous media |
| WO2018201180A1 (en) * | 2017-05-02 | 2018-11-08 | PETRA Data Science Pty Ltd | Automated, real time processing, analysis, mapping and reporting of data for the detection of geotechnical features |
| CN107314957A (en) * | 2017-06-30 | 2017-11-03 | 长安大学 | A kind of measuring method of rock fragmentation Size Distribution |
| CN108550154A (en) * | 2018-04-11 | 2018-09-18 | 中国科学院西双版纳热带植物园 | A kind of method of accurately measuring karst earth's surface bare rock accounting |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113177949A (en) * | 2021-04-16 | 2021-07-27 | 中南大学 | Large-size rock particle feature identification method and device |
| CN113177949B (en) * | 2021-04-16 | 2023-09-01 | 中南大学 | Large-size rock particle feature recognition method and device |
| CN113344945A (en) * | 2021-05-31 | 2021-09-03 | 沈阳工业大学 | Rock mass blasting blockiness automatic analysis device and method based on binocular vision |
| CN113344945B (en) * | 2021-05-31 | 2024-04-09 | 沈阳工业大学 | Automatic rock mass blasting block size analysis device and method based on binocular vision |
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