CN109655466A - A kind of spoil coal carrying rate online test method and device based on machine vision - Google Patents
A kind of spoil coal carrying rate online test method and device based on machine vision Download PDFInfo
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- 239000003245 coal Substances 0.000 title claims abstract description 70
- 238000010998 test method Methods 0.000 title claims description 10
- 238000001514 detection method Methods 0.000 claims abstract description 14
- 239000000463 material Substances 0.000 claims abstract description 7
- 238000000926 separation method Methods 0.000 claims abstract description 6
- 238000012545 processing Methods 0.000 claims abstract description 4
- 239000000284 extract Substances 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000003709 image segmentation Methods 0.000 claims description 3
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- 239000011521 glass Substances 0.000 claims 2
- 238000003708 edge detection Methods 0.000 claims 1
- 238000005259 measurement Methods 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000000034 method Methods 0.000 description 6
- 238000007689 inspection Methods 0.000 description 4
- 238000009434 installation Methods 0.000 description 3
- 238000012216 screening Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
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- 239000005338 frosted glass Substances 0.000 description 2
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/89—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles
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Abstract
本发明公开了一种基于机器视觉的矸石带煤率在线检测方法，包括以下步骤：S1、原煤分选后的矸石通过皮带输送机运输，皮带上的煤矸石到达遮光罩遮罩的范围内时，相机定期拍摄图像；S2、计算机对煤矸石图像进行实时的数字图像处理；S3、利用煤矸石的密度预测模型预测相应区域的密度，并区分每块区域是煤还是矸石，利用煤矸石的体积预测模型预测相应区域的体积，计算每块煤矸石的质量；S4、统计单张图像和一段时间内图像中煤的质量m与煤矸石的总质量M，通过公式计算矸石带煤率，获得实时值和平均值。本发明通过机器视觉技术对矸石进行矸石带煤率的快速预测，既避免了人为因素对测定结果的影响，又大大降低人力物力的消耗，并且调整分选设备参数。
The invention discloses an on-line detection method for gangue carrying rate based on machine vision, which includes the following steps: S1. The gangue after raw coal separation is transported by a belt conveyor, and when the coal gangue on the belt reaches the range of the shading cover , the camera regularly captures images; S2, the computer performs real-time digital image processing on the coal gangue image; S3, uses the coal gangue density prediction model to predict the density of the corresponding area, and distinguishes whether each area is coal or gangue, using the volume of coal gangue The prediction model predicts the volume of the corresponding area, and calculates the mass of each piece of coal gangue; S4. Count the mass m of coal and the total mass M of coal gangue in the single image and the image within a period of time, calculate the coal carrying rate of the gangue through the formula, and obtain real-time value and average. The invention uses machine vision technology to quickly predict the gangue carrying rate of gangue, which not only avoids the influence of human factors on the measurement result, but also greatly reduces the consumption of manpower and material resources, and adjusts the parameters of the sorting equipment.
Description
Technical field
The present invention relates to coal production technical fields, online more particularly to a kind of spoil coal carrying rate based on machine vision
Detection method and device.
Background technique
In coal separation production process, spoil coal carrying rate is an important technical indicator, reflects performance and the behaviour of screening installation
Make the level of personnel, therefore usually evaluates point of the equipment such as jigging machine, dense medium cyclone, compound sorting machine with spoil coal carrying rate
Effect is selected, is one of important technology performance assessment criteria.
The measurement of spoil coal carrying rate is that also have coal quality inspection personnel to hit with a hammer on Gangue knowledge by floating experiment
Other simple method, has following Railway Project:
1., need personnel largely to sample spoil in the process, consume manpower and material resources；
2., the sampling of the spoil of larger granularity when difficult, also increase the operation difficulty of floating experiment；
3., artificially sample when can exist only select pure spoil to avoid detect exceeded influences examination the phenomenon that so that sample
Under-represented, testing result is untrue；
4., entirely detection process takes long time, can not quickly learn screening installation effect adjusted so that equipment adjust
It is to take time and effort, causes the waste of energy resources.
Summary of the invention
In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a kind of, and the spoil coal carrying rate based on machine vision exists
Line detecting method and device substitute existing spoil coal carrying rate detection method, save human and material resources, improve the accurate of testing result
Property, realize on-line checking.
The technical scheme adopted by the invention is that: a kind of spoil coal carrying rate online test method based on machine vision, packet
Include following steps:
Spoil after S1, raw coal separation passes through belt haulage, and the gangue on belt reaches hood mask
When in range, computer control camera periodically shoots image and is transferred to computer；
S2, computer carry out real-time Digital Image Processing to the gangue image that camera takes: first passing through edge inspection
The image-region that every lump coal spoil in image is obtained with image segmentation is surveyed, all kinds of images of the coal gangue area after extracting segmentation are special
Sign；
S3, using gangue Forecasting Model of Density prediction corresponding region density, and distinguish every piece of region be coal or
Spoil calculates the quality of every lump coal spoil by the volume of gangue corresponding to every piece of region of volume predictions model prediction；
S4, statistics single image and in a period of time in image the quality m of coal and gangue gross mass M, pass through formula
Spoil coal carrying rate is calculated, real value and average value, the calculation formula of spoil coal carrying rate are obtained are as follows:
Spoil coal carrying rate=m/M × 100%
Further, image taking interval according to shooting area size and belt speed is set as 2-6s in step sl.
Further, the characteristics of image to be extracted in step s 2 has: extracting the r component, g component and b point of rgb space
Amount；H component, S component and the V component of HSV space；The gray value of gray space describes color, and extracts the one of color histogram
The color characteristic of rank square, second moment, third moment as image；Energy, contrast, correlation, the entropy of gray level co-occurrence matrixes are extracted,
Textural characteristics of the roughness, contrast, direction degree of Tamura texture as image；Extract the length of gangue minimum circumscribed rectangle
L, the wide B of gangue minimum circumscribed rectangle, the area A of coal gangue area, the perimeter P of coal gangue area are special as the geometry of image
Sign.
Further, the Forecasting Model of Density of gangue in step s3, for different grain size grade to extracted color
Feature and textural characteristics carry out feature selecting, and the Forecasting Model of Density of gangue is established by filtered out feature.
Further, the volume predictions model of gangue in step s3 are as follows:
ρ is the gangue density that Forecasting Model of Density predicts in formula.
Further, the detection device includes belt conveyor and detection device, and the detection device is arranged in belt
Above the middle part of conveyer.
Further, the detection device includes hood, computer, camera and LED light source, connection in the hood
There are camera and LED light source, and camera and LED light source are connected to a computer.
Further, the LED light source is 4, is symmetrically arranged at camera surrounding, the close hood inlet
LED light source on the outside of be equipped with frosted glass lamp shade.
Further, the hood includes metallic framework, and the metallic framework is wrapped with light-proof material, metal bone
A working space is formed in frame.
Compared with prior art, the beneficial effects of the present invention are: the present invention utilizes image recognition by machine vision technique
It realizes the on-line checking function of spoil coal carrying rate, in the transmission process after sorting the quick of spoil coal carrying rate is carried out to spoil
Prediction, had not only avoided influence of the human factor to measurement result, but also substantially reduce drain on manpower and material resources, and online pre- in real time
The timely adjustment for being conducive to screening installation parameter is surveyed, for improving sharpness of separation, promotes resources effective utilization to have particularly significant
Meaning.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow charts of the spoil coal carrying rate online test method of machine vision；
Fig. 2 is structure of the detecting device schematic diagram of the present invention；
Fig. 3 is camera and light source layout drawing in hood in the present invention
Wherein: 1- waits for measuring coal gangue, 2-LED light source, 3- camera, 4- computer, 5- hood, 6- belt conveyor.
Specific embodiment
In order to deepen the understanding of the present invention, present invention will be further explained below with reference to the attached drawings and examples, the implementation
Example for explaining only the invention, does not constitute protection scope of the present invention and limits.
As shown in Figure 1, a kind of spoil coal carrying rate online test method based on machine vision, comprising the following steps:
Spoil after S1, raw coal separation passes through belt haulage, and the gangue on belt reaches hood mask
When in range, computer control camera periodically shoots image and is transferred to computer；
S2, computer carry out real-time Digital Image Processing to the gangue image that camera takes: first passing through edge inspection
The image-region that every lump coal spoil in image is obtained with image segmentation is surveyed, all kinds of images of the coal gangue area after extracting segmentation are special
Sign；
S3, using gangue Forecasting Model of Density prediction corresponding region density, and distinguish every piece of region be coal or
Spoil calculates the quality of every lump coal spoil by the volume of gangue corresponding to every piece of region of volume predictions model prediction；
S4, statistics single image and in a period of time in image the quality m of coal and gangue gross mass M, pass through formula
Spoil coal carrying rate is calculated, real value and average value, the calculation formula of spoil coal carrying rate are obtained are as follows:
Spoil coal carrying rate=m/M × 100%
In the above-described embodiments, image-taking frequency need to be set according to shooting area size and belt speed in step sl
It is fixed, select 2 seconds as shooting interval, it is ensured that the gangue shot in image does not repeat.
In the above-described embodiments, the characteristics of image to be extracted in step s 2 has: extracting the r component of rgb space, g divides
Amount and b component；H component, S component and the V component of HSV space；The gray value of gray space describes color, and extracts color histogram
Color characteristic of the first moment, second moment, third moment of figure as image；Extract energy, the contrast, correlation of gray level co-occurrence matrixes
Property, entropy, textural characteristics of the roughness, contrast, direction degree of Tamura texture as image；Extract the minimum external square of gangue
The long L of shape, the wide B of gangue minimum circumscribed rectangle, the area A of coal gangue area, coal gangue area perimeter P as image
Geometrical characteristic.
In the above-described embodiments, utilize genetic algorithm (GA) to extracted color for different grain size grade in step s3
Feature and textural characteristics carry out feature selecting, and the density prediction mould of gangue is established by support vector machine classifier (SVM)
Type.
In the above-described embodiments, the volume predictions model of gangue in step s3 are as follows:
ρ is the gangue density that Forecasting Model of Density predicts in formula.
As shown in Fig. 2, a kind of spoil coal carrying rate on-line measuring device based on machine vision includes belt conveyor 6 and inspection
Measurement equipment, the detection device are arranged in above the middle part of belt conveyor 6；The detection device includes hood 5, computer
4, camera 3 and LED light source 2, are connected with camera 3 and LED light source 2 in the hood 5, and camera 3 and LED light source 2 with
Computer 4 is connected；The LED light source 2 is 4, is symmetrically arranged at 3 surrounding of camera, the close hood inlet
LED light source 2 on the outside of be equipped with frosted glass lamp shade；The hood 5 includes metallic framework, and the metallic framework is wrapped with opaque
Material, metallic framework is interior to form a working space.
What the embodiment of the present invention was announced is preferred embodiment, and however, it is not limited to this, the ordinary skill people of this field
Member, easily according to above-described embodiment, understands spirit of the invention, and make different amplification and variation, but as long as not departing from this
The spirit of invention, all within the scope of the present invention.
Claims (9)
1. a kind of spoil coal carrying rate online test method based on machine vision, which comprises the following steps:
Spoil after S1, raw coal separation passes through belt haulage, and the gangue on belt reaches the range of hood mask
When interior, computer control camera periodically shoots image and is transferred to computer；
The gangue image that S2, computer take camera carries out real-time Digital Image Processing: first pass through edge detection and
Image segmentation obtains the image-region of every lump coal spoil in image, all kinds of characteristics of image of the coal gangue area after extracting segmentation；
S3, using gangue Forecasting Model of Density prediction corresponding region density, and distinguishing every piece of region is coal or spoil,
By the volume of gangue corresponding to every piece of region of volume predictions model prediction, the quality of every lump coal spoil is calculated；
S4, statistics single image and in a period of time in image the quality m of coal and gangue gross mass M, calculated by formula
Spoil coal carrying rate obtains real value and average value, the calculation formula of spoil coal carrying rate are as follows:
Spoil coal carrying rate=m/M × 100%.
2. the spoil coal carrying rate online test method according to claim 1 based on machine vision, it is characterised in that: in step
Image taking interval is set as 2-6s according to shooting area size and belt speed in rapid S1.
3. the spoil coal carrying rate online test method according to claim 1 based on machine vision, it is characterised in that: in step
There is the characteristics of image to be extracted in rapid S2: extracting the r component, g component and b component of rgb space；The H component of HSV space, S points
Amount and V component；The gray value of gray space describes color, and extracts the first moment, second moment, third moment conduct of color histogram
The color characteristic of image；Energy, contrast, correlation, the entropy of gray level co-occurrence matrixes are extracted, it is the roughness of Tamura texture, right
Textural characteristics than degree, direction degree as image；Extract long L, the gangue minimum circumscribed rectangle of gangue minimum circumscribed rectangle
Wide B, the area A of coal gangue area, coal gangue area geometrical characteristic of the perimeter P as image.
4. the spoil coal carrying rate online test method according to claim 1 based on machine vision, it is characterised in that: in step
The Forecasting Model of Density of gangue in rapid S3 carries out feature to extracted color characteristic and textural characteristics for different grain size grade
Selection, and pass through the Forecasting Model of Density that filtered out feature establishes gangue.
5. the spoil coal carrying rate online test method according to claim 1 based on machine vision, it is characterised in that: in step
The volume predictions model of gangue in rapid S3 are as follows:
ρ is the gangue density that Forecasting Model of Density predicts in formula.
6. the spoil coal carrying rate on-line measuring device according to claim 1 based on machine vision, it is characterised in that: described
Detection device includes belt conveyor and detection device, and the detection device is arranged in above the middle part of belt conveyor.
7. the spoil coal carrying rate on-line measuring device according to claim 6 based on machine vision, it is characterised in that: described
Detection device includes hood, computer, camera and LED light source, and camera and LED light source are connected in the hood, and
Camera and LED light source are connected to a computer.
8. the spoil coal carrying rate on-line measuring device according to claim 7 based on machine vision, it is characterised in that: described
LED light source is 4, is symmetrically arranged at camera surrounding, is equipped with hair glass on the outside of the LED light source of the close hood inlet
Glass cover.
9. the spoil coal carrying rate on-line measuring device according to claim 7 based on machine vision, it is characterised in that: described
Hood includes metallic framework, and the metallic framework is wrapped with light-proof material, forms a working space in metallic framework.
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Cited By (8)
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| CN110441320A (en) * | 2019-08-05 | 2019-11-12 | 北京泰豪信息科技有限公司 | A kind of gangue detection method, apparatus and system |
| CN111036576A (en) * | 2019-12-10 | 2020-04-21 | 清远职业技术学院 | Gangue identification and sorting method based on gangue-free image filtering and BLOB analysis |
| CN111811981A (en) * | 2020-09-03 | 2020-10-23 | 天津美腾科技股份有限公司 | Coal content detection method, device and system |
| CN111896544A (en) * | 2020-08-05 | 2020-11-06 | 合肥约翰芬雷矿山装备有限公司 | On-line combustion value detection method and detection device for coal preparation |
| CN112330607A (en) * | 2020-10-20 | 2021-02-05 | 精英数智科技股份有限公司 | Coal and gangue identification method, device and system based on image identification technology |
| CN112446914A (en) * | 2020-12-04 | 2021-03-05 | 中国矿业大学(北京) | Coal gangue quality calculation method and system in top coal caving process |
| CN113695266A (en) * | 2021-08-26 | 2021-11-26 | 天地（常州）自动化股份有限公司 | Visual device for gangue selection |
| CN116060321A (en) * | 2023-03-14 | 2023-05-05 | 天津美腾科技股份有限公司 | Coal gangue sorting and adjusting method and device and nonvolatile storage medium |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110441320A (en) * | 2019-08-05 | 2019-11-12 | 北京泰豪信息科技有限公司 | A kind of gangue detection method, apparatus and system |
| CN111036576A (en) * | 2019-12-10 | 2020-04-21 | 清远职业技术学院 | Gangue identification and sorting method based on gangue-free image filtering and BLOB analysis |
| CN111896544A (en) * | 2020-08-05 | 2020-11-06 | 合肥约翰芬雷矿山装备有限公司 | On-line combustion value detection method and detection device for coal preparation |
| CN111811981A (en) * | 2020-09-03 | 2020-10-23 | 天津美腾科技股份有限公司 | Coal content detection method, device and system |
| CN112330607A (en) * | 2020-10-20 | 2021-02-05 | 精英数智科技股份有限公司 | Coal and gangue identification method, device and system based on image identification technology |
| CN112446914A (en) * | 2020-12-04 | 2021-03-05 | 中国矿业大学(北京) | Coal gangue quality calculation method and system in top coal caving process |
| CN112446914B (en) * | 2020-12-04 | 2023-08-15 | 中国矿业大学(北京) | Gangue quality calculation method and system in top coal caving process |
| CN113695266A (en) * | 2021-08-26 | 2021-11-26 | 天地（常州）自动化股份有限公司 | Visual device for gangue selection |
| CN116060321A (en) * | 2023-03-14 | 2023-05-05 | 天津美腾科技股份有限公司 | Coal gangue sorting and adjusting method and device and nonvolatile storage medium |
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