CN109269951B - Image-based flotation tailing ash content, concentration and coarse particle content detection method - Google Patents
Image-based flotation tailing ash content, concentration and coarse particle content detection method Download PDFInfo
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Abstract
The invention relates to the technical field of flotation tailing mine, and provides a method for detecting ash content, concentration and coarse particle content of flotation tailing based on images, which respectively extracts images aiming at flotation tailings with different ash content, concentration and coarse particle content and establishes a feature library based on image features; constructing an ash content, concentration and coarse particle content prediction model of the flotation tailings according to the feature library; and extracting the flotation tailing image to be detected, and determining the ash content, the concentration and the coarse particle content of the flotation tailings according to the prediction model. The invention has the beneficial effects that: the problem of large subjective factors of manual observation is avoided; the detection time is short, the frequency is high, and the production requirement is met; high efficiency, reduced workman working strength, can realize that the workshop subtracts the personnel, reduce the production cost of labor.
Description
Technical Field
The invention relates to the technical field of flotation tailing mine, in particular to a method for detecting ash content, concentration and coarse particle content of flotation tailing based on images.
Background
Along with the continuous improvement of the automation overall level of the industrial process, the automatic control and the intelligent control of the coal slime flotation process are more and more emphasized by people. One of the key links of the intelligent control of the flotation process is to realize the online detection of the product quality, including flotation of clean coal and tail coal. However, the lack of related detection technologies and sensors severely limits the development of intelligent flotation. The quality detection research of flotation products always focuses on flotation of clean coal, and flotation of tail coal is neglected. The flotation tailing quality is used as important feedback information and plays a crucial role in realizing closed-loop optimization control of the flotation process.
In the actual production process of coal slime flotation, field operators mainly rely on naked eyes to visually observe the color of tailings to judge ash content, judge whether flotation tailings have the problem of coarse flotation through hand grasping feeling, and adjust variables such as reagent dosage, foam layer thickness, aeration quantity and the like on the basis, so that the quality of flotation products is guaranteed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for detecting the ash content, the concentration and the coarse particle content of flotation tailings based on images, which utilizes a machine vision method to detect the ash content, the concentration and the coarse particle content of the flotation tailings.
The technical scheme of the invention is as follows:
an image-based flotation tailing ash content, concentration and coarse particle content detection method comprises the following steps:
respectively extracting images aiming at flotation tailings with different ash contents, concentrations and coarse particle contents, and establishing a feature library based on image features;
constructing an ash content, concentration and coarse particle content prediction model of the flotation tailings according to the feature library;
and extracting the flotation tailing image to be detected, and determining the ash content, the concentration and the coarse particle content of the flotation tailing according to the prediction model.
Further, an image analyzed by ash content detection is shot by an image acquisition system consisting of a fixed-focus lens, a color CCD industrial camera and an annular LED light source, and a direct illumination mode is adopted; the image analyzed by concentration detection and coarse particle content detection is shot by an image acquisition system consisting of a double-telecentric lens, a black-and-white CCD industrial camera and a parallel light source, and a backlight transmission illumination mode is adopted.
Further, the ash detection comprises the following steps:
step 1, collecting flotation tailing samples and color images with different concentrations and different ash contents, and classifying according to the ash content testing result;
step 2, preprocessing the image and removing noise in the image;
step 3, R, G, B, Y, U, V, H, S, I component characteristics of the image in RGB, YUV and HSI color space domains are extracted;
step 4, establishing a regression model of the ash content of the tailings with the same concentration, performing function fitting by using a least square method to obtain a formula coefficient and a fitting result, and selecting a color difference variable C formed by linear combination of R, G, B three components according to the model fitting effectrgbThe method is used as a model independent variable to establish a soft measurement model of the ash content of the coal slurry with the same concentration, and the model form is as follows:
wherein y is the model ash output, a1、b1、c1、k1、k2Is a coefficient;
and 5, introducing a concentration correction function as follows:
wherein M issRepresenting the concentration of the tailing coal slurry, and j is an unknown coefficient;
step 6, constructing a soft measurement model form of the ash content of the flotation tail coal as follows:
Ash＝a1ln{(k1R+k2G-B)/[1+exp(j*Ms)]+b1}+c1
wherein Ash represents the Ash content of the tail coal.
And 7, analyzing the flotation tailing image to be detected by using the soft measurement model, and detecting the ash content of the tailing.
Further, the concentration detection comprises the following steps:
step 1, collecting flotation tailing samples with different concentrations and black-and-white images, and classifying according to the result of the concentration test;
step 2, preprocessing the image and removing noise in the image;
step 3, extracting the gray characteristic of the image;
step 4, establishing a prediction model of the coal slurry concentration, selecting an image gray mean value to establish a soft measurement model of the coal slurry concentration, and performing function fitting by using a least square method to obtain a formula coefficient and a fitting result, wherein the model form is as follows:
wherein M isSFor flotation tailing concentration, Gray is image Gray average value, a2、b2Are coefficients.
And 5, analyzing the flotation tailing image to be detected by using the soft measurement model, and detecting the concentration of the tailings.
Further, the concentration detection comprises the following steps:
step 1, collecting flotation tailing samples with different concentrations and black-and-white images, and classifying according to the result of the concentration test;
step 2, calibrating a camera by adopting an optical glass checkerboard, determining checkerboard angular points by utilizing a Harris angular point algorithm, then calculating the number of pixels between every two vertically and horizontally adjacent vertexes in a visual field range, namely the Euclidean distance between the adjacent vertexes, and then calculating the real size represented by each pixel point;
step 3, preprocessing the image and removing noise in the image;
step 4, carrying out binarization processing on the preprocessed image, and then eliminating a large amount of agglomerated non-target particles while smoothing the edges of the target particles through morphological processing; in order to avoid the influence of incomplete particle images on the analysis result of the field of view boundary, the particles connected with the image boundary need to be eliminated;
step 5, a watershed algorithm is controlled to segment the adhered particles in the image based on the distance variable and the marker; first, a distance transformation is to be carried out, by means of mathematical morphologyExtracting local maximum value points in the image after distance transformation by using the extended maximum transformation H-maximum technology, and combining the local maximum value points by using a morphological dilation method to obtain internal mark points and an internal mark image fgmEliminating over-segmentation, obtaining external marker and external marker image f by one time of watershed operationbm(ii) a Then based on the obtained internal and external marks, modifying the original gradient image by using a forced minimum technology, and then performing secondary watershed operation to finally complete segmentation;
step 6, carrying out edge detection on the segmented particle image, and then extracting the quantity distribution and geometric parameters of the target coal particles;
step 7, establishing a flotation tailing coarse particle content prediction model, respectively shooting a plurality of pictures, counting the particle size and the coarse particle number, calculating the average value of the coarse particle number, performing function fitting by using a least square method to obtain a formula coefficient and a fitting result, and finally establishing the flotation tailing coarse particle content prediction model, wherein the form is as follows:
y＝a3x+b3
y represents the coarse particle content of the flotation tailings; x represents the number of coarse particles measured by image method, a3、b3Are coefficients.
And 8, analyzing the flotation tailing image to be detected by using the soft measurement model, and detecting the coarse particle content of the tailing.
Further, in step 6, the geometric parameters of the target coal particles include: particle area, particle perimeter, equivalent circle diameter, particle shape index, and ovality parameters.
Further, in step 7, the coal particle size characterization based on the image method adopts an equivalent circle diameter, namely a particle projection area equivalent diameter.
Further, the particle size of the coarse particles of the flotation tail coal is larger than 250 um.
The invention has the beneficial effects that: the problem of large subjective factors of manual observation is avoided; the detection time is short, the frequency is high, and the production requirement is met; high efficiency, reduced workman working strength, can realize that the workshop subtracts the personnel, reduce the production cost of labor.
Detailed Description
Hereinafter, specific embodiments of the present invention will be described in detail. It should be noted that technical features or combinations of technical features described in the following embodiments should not be considered as being isolated, and they may be combined with each other to achieve better technical effects.
The experimental sample of the embodiment of the invention is from a Liuwan coal preparation plant, and the process of a coal preparation second workshop of the Liuwan coal preparation plant comprises the following steps: raw coal of 50-1mm size fraction is sorted by a dense medium cyclone, the raw coal below 1mm is classified by a classification concentration cyclone, the classification granularity is 0.25mm, wherein the overflow of 1-0.25mm size fraction is sorted by a TBS coarse coal slime sorting machine, and the coal slime of 0.25-0mm size fraction enters 4 XJM-S-20 flotation systems. The research object of the embodiment is tail coal of coal slime flotation.
The embodiment of the invention provides a detection method for analyzing ash content, concentration and coarse particle content based on a flotation tailing image. In the invention, an image analyzed by ash content detection is shot by an image acquisition system consisting of a fixed-focus lens, a color CCD industrial camera and an annular LED light source, and a direct illumination mode is adopted; the image analyzed by concentration detection and coarse particle content detection is shot by an image acquisition system consisting of a double telecentric lens, a black-and-white CCD industrial camera and a parallel light source, and a backlight transmission illumination mode is adopted. The method comprises the steps of firstly, constructing a feature library based on image features by using a traditional detection means of the ash content, the concentration and the coarse particle content of the tailings, and constructing a prediction model of the ash content, the concentration and the coarse particle content of the tailings.
The embodiment of the invention adopts the technical scheme that:
firstly, ash content detection:
1. collecting flotation tailing samples with different concentrations and ash contents and color images by professionals, and classifying according to the result of ash content testing;
2. the image is preprocessed, noise in the image is removed, image quality is improved, and useful information is highlighted.
3. Extracting R, G, B, Y, U, V, H, S, I component characteristics of the image in RGB, YUV and HSI color space domains;
4. establishing a regression model of the ash content of the tailings with the same concentration, screening the existing color characteristic parameters and a series of self-established characteristic parameters, performing function fitting by using a least square method to obtain a formula coefficient and a fitting result, and selecting a color difference variable Crgb formed by R, G, B three-component linear combination as a model independent variable to establish a soft measurement model of the ash content of the coal slurry with the same concentration according to the model fitting effect, wherein the model form is as follows:
wherein y is the ash output result of the regression model of the tailings ash with the same concentration.
The fitting parameters and evaluation indices are shown in table 1.
TABLE 1 Soft-measurement model fitting parameters and evaluation indexes for ash content of tailings
In the table r2For determining the coefficients, the method is mainly used for evaluating the fitting effect and the quality, r2The closer to 1, the better the fitting effect of the regression equation is explained; r is2Closer to 0 indicates a poorer fit. RMSE is the root mean square error, MRE is the average relative error, MaxRE is the maximum relative error.
5. Introducing a concentration correction function
Is concentration pair CrgbCorrection function of value, MsRepresenting the tail coal slurry concentration. The soft measurement model form of the ash content of the flotation tail coal is constructed as follows:
Ash＝31.171ln{(0.266R+0.87G-B)/[1+exp(-0.186Ms)]+1.592}-34.026
wherein Ash represents the Ash content of the tail coal.
Secondly, concentration detection:
1. collecting flotation tailing samples with different concentrations and black and white images by professionals, and classifying according to the result of the concentration of the assay;
2. the image is preprocessed, noise in the image is removed, image quality is improved, and useful information is highlighted.
3. Extracting gray features of the image;
4. establishing a coal slurry concentration prediction model, screening the existing gray characteristic parameters, performing function fitting by using a least square method to obtain a formula coefficient and a fitting result, and selecting an image gray mean value to establish a coal slurry concentration soft measurement model according to the model fitting effect, wherein the model form is as follows:
wherein MS is the concentration of the flotation tailings.
The fitting parameters and evaluation indices are shown in table 2.
TABLE 2 concentration prediction model fitting parameters and evaluation indices
Thirdly, detecting the content of coarse particles:
1. collecting flotation tailing samples with different coarse particle contents and black and white images by professionals, and classifying according to coarse particle content testing results;
2. the method comprises the steps of adopting an optical glass checkerboard calibration camera, determining checkerboard angular points by using a Harris angular point algorithm, then calculating the number of pixels between two vertically and horizontally adjacent vertexes in a field range, namely the Euclidean distance between the adjacent vertexes, and then calculating the real size represented by each pixel point.
3. The image is preprocessed, noise in the image is removed, image quality is improved, and useful information is highlighted.
4. And carrying out binarization processing on the preprocessed image, and then eliminating a large amount of agglomerated non-target particles while smoothing the edges of the target particles through morphological processing. In order to avoid that the incomplete grain image at the boundary of the field of view affects the analysis result, the grains connected with the image boundary need to be eliminated.
5. And (4) applying a watershed algorithm controlled based on the distance variable and the marker to segment the conglutinated particles in the image. Firstly, distance transformation is required to be carried out, local maximum value points in an image after the distance transformation are extracted by means of an extended maximum transformation (H-maximum) technology in mathematical morphology, local maximum value points are combined by adopting a morphological middle expansion method to obtain internal mark points and an internal mark image fgm, over-segmentation is eliminated, an external mark symbol and an external mark image fbm are obtained through watershed operation for one time, then the original gradient image is modified by utilizing a forced minimum technology based on the obtained internal mark and the external mark, and then the secondary watershed operation is carried out, so that an ideal segmentation effect is finally obtained.
6. And (3) carrying out edge detection on the segmented particle image, and then extracting the quantity distribution and geometric parameters (particle area, particle perimeter, equivalent circle diameter, particle shape index, ellipticity parameter and the like) of the target coal particles.
7. Establishing a flotation tailing coarse particle content prediction model, respectively shooting five pictures, and counting the particle size and the number of coarse particles (the particle size is larger than 250um) by using the method described above, wherein the coal particle size characterization based on an image method adopts an equivalent circle diameter, namely a particle projection area equivalent diameter, and calculates the average value of the number of the coarse particles, the average value of the number of the coarse particles detected by the image method and the content of the coarse particles present an obvious linear relation, and a least square method is used for performing function fitting to obtain a formula coefficient and a fitting result, so that the finally established flotation tailing coarse particle content prediction model is in the form of the following formula
y＝0.2342x+2.0655
y represents the coarse particle content of the flotation tailings; x represents the number of coarse particles measured by image method.
The results of the measurements of the coal slurry image particles with different coarse particle contents are shown in table 3.
TABLE 3 coal slurry image particle detection results with different coarse particle contents
While several embodiments of the present invention have been presented herein, it will be appreciated by those skilled in the art that changes may be made to the embodiments herein without departing from the spirit of the invention. The above-described embodiments are merely exemplary and should not be taken as limiting the scope of the invention.
Claims (5)
1. The method for detecting the ash content, the concentration and the coarse particle content of the flotation tailings based on the image is characterized by comprising the following steps: hardware structure aspect: the multi-parameter detection system based on the image is shot by an image acquisition system consisting of a fixed-focus lens, a color CCD industrial camera and an annular LED light source, and the ash content detection is realized by adopting a direct illumination mode; the device consists of a double telecentric lens, a black-and-white CCD industrial camera and a parallel light source, and realizes concentration detection and coarse particle content detection by adopting a backlight transmission illumination mode; in the aspect of algorithm: respectively extracting images aiming at flotation tailings with different ash contents, concentrations and coarse particle contents, and establishing an image-based feature library; constructing an ash content, concentration and coarse particle content prediction model of the flotation tailings according to the feature library; extracting an image of the flotation tailings to be detected, and determining the ash content, the concentration and the coarse particle content of the flotation tailings according to the prediction model;
the ash content detection comprises the following steps:
step 1, collecting flotation tailing samples and color images with different concentrations and different ash contents, and classifying according to the ash content testing result;
step 2, preprocessing the image and removing noise in the image;
step 3, R, G, B, Y, U, V, H, S, I component characteristics of the image in RGB, YUV and HSI color space domains are extracted;
step 4, establishing a regression model of the ash content of the tailings with the same concentration, performing function fitting by using a least square method to obtain a formula coefficient and a fitting result, and selecting a color difference variable C formed by linear combination of R, G, B three components according to the model fitting effectrgbThe method is used for establishing a soft measurement model of the ash content of the coal slurry with the same concentration as a model independent variable, and the model form is as follows:
wherein y is the model ash output, a1、b1、c1、k1、k2Is a coefficient;
and 5, introducing a concentration correction function as follows:
wherein M issRepresenting the concentration of the tailing coal slurry, and j is a coefficient;
step 6, constructing a soft measurement model form of the ash content of the flotation tail coal as follows:
Ash＝a1ln{(k1R+k2G-B)/[1+exp(j*Ms)]+b1}+c1
wherein Ash represents the Ash content of the tail coal;
and 7, analyzing the flotation tailing image to be detected by using the soft measurement model in the step 6, and detecting the ash content of the tailings.
2. The method of claim 1, wherein the concentration detection comprises the steps of:
step 1, collecting flotation tailing samples with different concentrations and black-and-white images, and classifying according to the result of the concentration test;
step 2, preprocessing the image and removing noise in the image;
step 3, extracting the gray characteristic of the image;
step 4, establishing a prediction model of the coal slurry concentration, selecting an image gray mean value to establish a soft measurement model of the coal slurry concentration, and performing function fitting by using a least square method to obtain a formula coefficient and a fitting result, wherein the model form is as follows:
wherein M isSFor flotation tailing concentration, Gray is image Gray average value, a2、b2Is a coefficient;
and 5, analyzing the flotation tailing image to be detected by using the model in the step 4, and detecting the concentration of the tailing.
3. The method of claim 1, wherein the coarse content detection comprises the steps of:
step 1, collecting flotation tailing samples with different coarse particle contents and black-and-white images, and classifying according to coarse particle content testing results;
step 2, adopting an optical glass checkerboard calibration camera, determining checkerboard angular points by using a Harris angular point algorithm, then calculating the number of pixels between all two vertically and horizontally adjacent vertexes in a visual field range, namely the Euclidean distance between the adjacent vertexes, and then calculating the real size represented by each pixel point;
step 3, preprocessing the image and removing noise in the image;
step 4, carrying out binarization processing on the preprocessed image, and then eliminating a large amount of agglomerated non-target particles while smoothing the edges of the target particles through morphological processing; in order to avoid the influence of incomplete particle images on the analysis result of the field of view boundary, the particles connected with the image boundary need to be eliminated;
step 5, a watershed algorithm is controlled to segment the adhered particles in the image based on the distance variable and the marker; first, a distance transformation is to be carried out, using mathematical modalitiesExtracting local maximum points in the image after distance transformation by using an extended maximum transformation H-maximum technology in science, and combining the local maximum points by using a morphological expansion method to obtain internal mark points and an internal mark image fgmEliminating over-segmentation, obtaining external marker and external marker image f by one time of watershed operationbm(ii) a Then, based on the obtained internal and external marks, modifying the original gradient image by using a forced minimum technology, and then performing secondary watershed operation to finally complete segmentation;
step 6, carrying out edge detection on the segmented particle image, and then extracting the quantity distribution and geometric parameters of the target coal particles;
step 7, establishing a flotation tailing coarse particle content prediction model, respectively shooting a plurality of pictures, counting the particle size and the coarse particle number, calculating the average value of the coarse particle number, wherein the average value of the coarse particle number detected by an image method and the coarse particle content present an obvious linear relation, performing function fitting by using a least square method to obtain a formula coefficient and a fitting result, and finally establishing the flotation tailing coarse particle content prediction model, wherein the form is as follows:
y＝a3x+b3
y represents the coarse particle content of the flotation tailings; x represents the number of coarse particles measured by image method, a3、b3Is a coefficient;
and 8, analyzing the image of the flotation tailings to be detected by using the model in the step 7, and detecting the content of coarse particles in the tailings.
4. The method of claim 3, wherein in step 6, the geometric parameters of the target coal particles comprise: particle area, particle perimeter, equivalent circle diameter, particle shape index, and ovality parameters.
5. The method of claim 3, wherein in step 7, the coal particle size characterization based on the image method adopts equivalent circle diameter, namely equivalent diameter of particle projection area, and coarse particles refer to particles with equivalent circle diameter larger than 250 um.
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