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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- CN109655466A CN109655466A CN201910016489.4A CN201910016489A CN109655466A CN 109655466 A CN109655466 A CN 109655466A CN 201910016489 A CN201910016489 A CN 201910016489A CN 109655466 A CN109655466 A CN 109655466A
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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
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Abstract
The spoil coal carrying rate online test method based on machine vision that the invention discloses a kind of, the following steps are included: the spoil after S1, raw coal separation passes through belt haulage, when in the range of the gangue arrival hood mask on belt, camera periodically shoots image；S2, computer carry out real-time Digital Image Processing to gangue image；S3, using gangue Forecasting Model of Density prediction corresponding region density, and distinguishing every piece of region is that coal or spoil using the volume of the volume predictions model prediction corresponding region of gangue calculate the quality of every lump coal spoil；S4, statistics single image and in a period of time in image the quality m of coal and gangue gross mass M, spoil coal carrying rate is calculated by formula, obtains real value and average value.The present invention carries out the quick predict of spoil coal carrying rate by machine vision technique to spoil, has not only avoided influence of the human factor to measurement result, but also substantially reduce drain on manpower and material resources, and adjust screening installation parameter.
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)
| 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 |
| 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 | Online combustion value detection method and detection device for coal dressing |
| 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 |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1763677A (en) * | 2005-10-17 | 2006-04-26 | 太原理工大学 | Intelligent discharging control system of jig |
| CN102798470A (en) * | 2012-08-14 | 2012-11-28 | 中国神华能源股份有限公司 | Method and device for monitoring range of heat abnormality in mining area |
| CN103218619A (en) * | 2013-03-15 | 2013-07-24 | 华南理工大学 | Image aesthetics evaluating method |
| CN103310460A (en) * | 2013-06-24 | 2013-09-18 | 安科智慧城市技术（中国）有限公司 | Image characteristic extraction method and system |
| CN104794426A (en) * | 2015-01-13 | 2015-07-22 | 宁夏医科大学 | Method for improving prostate tumor MRI (Magnetic Resonance Imaging) image identification rate based on CAD (Computer-Aided Diagnosis) system |
| CN105651713A (en) * | 2015-12-30 | 2016-06-08 | 浙江工业大学 | Quantitative determination method for chlorophyll of green vegetable leaves based on computer image analysis |
| CN106269576A (en) * | 2016-09-12 | 2017-01-04 | 中国矿业大学 | A kind of Automatic Selection System of Waste Rock from Coal Bulk and method |
| WO2017040674A1 (en) * | 2015-08-31 | 2017-03-09 | Covar Applied Technologies, Inc. | System and method for estimating damage to a shaker table screen using computer vision |
| CN108267172A (en) * | 2018-01-25 | 2018-07-10 | 神华宁夏煤业集团有限责任公司 | Mining intelligent robot inspection system |
| CN108380525A (en) * | 2018-03-06 | 2018-08-10 | 深圳市时维智能装备有限公司 | Bastard coal device for visual identification and method |
| CN108805165A (en) * | 2018-04-27 | 2018-11-13 | 淘然视界(杭州)科技有限公司 | A kind of coal identification method for sorting, electronic equipment, storage medium and system |
-
2019
- 2019-01-08 CN CN201910016489.4A patent/CN109655466A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1763677A (en) * | 2005-10-17 | 2006-04-26 | 太原理工大学 | Intelligent discharging control system of jig |
| CN102798470A (en) * | 2012-08-14 | 2012-11-28 | 中国神华能源股份有限公司 | Method and device for monitoring range of heat abnormality in mining area |
| CN103218619A (en) * | 2013-03-15 | 2013-07-24 | 华南理工大学 | Image aesthetics evaluating method |
| CN103310460A (en) * | 2013-06-24 | 2013-09-18 | 安科智慧城市技术（中国）有限公司 | Image characteristic extraction method and system |
| CN104794426A (en) * | 2015-01-13 | 2015-07-22 | 宁夏医科大学 | Method for improving prostate tumor MRI (Magnetic Resonance Imaging) image identification rate based on CAD (Computer-Aided Diagnosis) system |
| WO2017040674A1 (en) * | 2015-08-31 | 2017-03-09 | Covar Applied Technologies, Inc. | System and method for estimating damage to a shaker table screen using computer vision |
| CN105651713A (en) * | 2015-12-30 | 2016-06-08 | 浙江工业大学 | Quantitative determination method for chlorophyll of green vegetable leaves based on computer image analysis |
| CN106269576A (en) * | 2016-09-12 | 2017-01-04 | 中国矿业大学 | A kind of Automatic Selection System of Waste Rock from Coal Bulk and method |
| CN108267172A (en) * | 2018-01-25 | 2018-07-10 | 神华宁夏煤业集团有限责任公司 | Mining intelligent robot inspection system |
| CN108380525A (en) * | 2018-03-06 | 2018-08-10 | 深圳市时维智能装备有限公司 | Bastard coal device for visual identification and method |
| CN108805165A (en) * | 2018-04-27 | 2018-11-13 | 淘然视界(杭州)科技有限公司 | A kind of coal identification method for sorting, electronic equipment, storage medium and system |
Non-Patent Citations (8)
| Title |
|---|
| D. DOU ET AL.: "Coal and gangue recognition under four operating conditions by using image analysis and Relief-SVM", 《INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION》 * |
| KUIDONG GAO ET AL.: "An Efficient of Coal and Gangue Recognition Algorithm", 《INTERNATIONAL JOURNAL OF SIGNAL PROCESSING, IMAGE PROCESSING AND PATTERN RECOGNITION》 * |
| ZHAO MING-HUI: "Intelligent Sorting System of Coal Gangue with Machine Vision", 《2018 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS》 * |
| 张泽琳: "基于机器视觉的煤质快速分析方法研究", 《中国博士学位论文全文数据库信息科技辑》 * |
| 张泽琳等: "基于MATLAB的煤粒图像识别系统及其密度和产率的预测", 《选煤技术》 * |
| 张泽琳等: "基于图像分析的粗粒煤堆密度组成估计", 《中国矿业大学学报》 * |
| 郭德，张秀梅: "《选煤新技术》", 31 July 2018, 北京：煤炭工业出版社 * |
| 鲁恒润等: "基于机器视觉的煤矸特征提取与分类研究", 《煤炭工程》 * |
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 | Online combustion value detection method and detection device for coal dressing |
| 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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Application publication date: 20190419 |