CN113096149B - Shaking table ore belt segmentation method based on three color elements - Google Patents
Shaking table ore belt segmentation method based on three color elements Download PDFInfo
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- CN113096149B CN113096149B CN201911334684.8A CN201911334684A CN113096149B CN 113096149 B CN113096149 B CN 113096149B CN 201911334684 A CN201911334684 A CN 201911334684A CN 113096149 B CN113096149 B CN 113096149B
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000011218 segmentation Effects 0.000 title abstract description 7
- 239000012141 concentrate Substances 0.000 claims abstract description 19
- 238000004891 communication Methods 0.000 claims abstract description 13
- 230000000877 morphologic effect Effects 0.000 claims abstract description 5
- 238000007781 pre-processing Methods 0.000 claims abstract description 5
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 239000000758 substrate Substances 0.000 claims 3
- 238000012512 characterization method Methods 0.000 claims 1
- 239000011435 rock Substances 0.000 claims 1
- 238000010586 diagram Methods 0.000 abstract description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 2
- 229910052500 inorganic mineral Inorganic materials 0.000 description 6
- 239000011707 mineral Substances 0.000 description 6
- 238000000926 separation method Methods 0.000 description 3
- 239000003086 colorant Substances 0.000 description 2
- 238000011084 recovery Methods 0.000 description 2
- ATJFFYVFTNAWJD-UHFFFAOYSA-N Tin Chemical compound [Sn] ATJFFYVFTNAWJD-UHFFFAOYSA-N 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005192 partition 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
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/155—Segmentation; Edge detection involving morphological operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/187—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
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The application relates to a shaking table ore belt segmentation method based on three color elements. The method comprises the following steps: collecting images of a shaking table ore belt; preprocessing an image; acquiring color characteristic parameters of a shaking table concentrate belt; calculating to obtain a similarity gray level map; carrying out morphological open operation on the similarity gray level diagram and obtaining a communication region; and obtaining a rectangular area of concentrate. The application utilizes the information of brightness, tone and saturation of the image of the mine belt, is more in line with the model for distinguishing the mine belt by human eyes during manual operation, can well solve the difficult problems of water bubbles, light source reflection and the like, and has strong robustness.
Description
Technical Field
The application relates to the technical field of mineral separation, in particular to a shaking table ore belt segmentation method based on three color elements.
Background
At present, in the process of concentrating ore by using a shaking table, the separation of ore strips formed on the shaking table adopts a manual method. The manual operation mode of the cradle often causes the occurrence of the adverse conditions of untimely adjustment of the ore receiving plate, inaccurate placement of the ore receiving plate and the like, and the reason for the reason is that: large process condition change, uneven job level, poor responsibility consciousness of partial job, limited analysis and judgment capability and other factors. Different operators judge different sector color partitions of the shaking table and operate different, and the large difference of mineral separation indexes can occur, so that the operation mode completely relying on manual experience and responsibility consciousness is relatively extensive, and has poor consistency, poor instantaneity and poor accuracy, so that the concentrate grade and the mineral recovery level are low and the fluctuation is large, and the mineral recovery rate cannot be effectively improved. In order to solve the problem, it is proposed to divide the color ore belt image formed on the table surface in real time by using the machine vision technique. The method does not need manual intervention, and can directly utilize the difference of three element characteristics of different mineral colors to digitally identify and segment the mineral belt images on the cradle bed surface in real time.
Disclosure of Invention
The application aims to solve the technical problem of providing a shaking table ore belt segmentation method based on three color elements, which fully utilizes the information of brightness, tone and saturation of an ore belt image, is more in line with a model for distinguishing the ore belt by human eyes during manual operation, can well treat the difficult problems of blister, light source reflection and the like, and has strong robustness.
Technical objects that may be achieved by the present application are not limited to what has been particularly described hereinabove, and other technical objects not described herein will be more clearly understood by those skilled in the art from the following detailed description.
The technical scheme for solving the technical problems is as follows:
according to an aspect of the disclosure, the application provides a method for cutting a shaking table ore belt based on three color elements, which comprises the following steps:
s1: acquiring images of a shaking table ore belt, wherein the shaking table ore belt is photographed by an industrial camera, the acquired images are input into a computer, and the color space of the images is RGB space;
s2: preprocessing the image, wherein the acquired image is subjected to cutting, blurring and scaling to obtain a processed image;
s3: performing color space conversion, wherein the processed image is subjected to color space conversion from RGB space to tone, saturation, brightness space;
s4: acquiring color characteristic parameters of a shaking table concentrate belt, wherein a color range is set according to the color of the selected concentrate, all pixel points of the processed image are traversed, and the color characteristic parameters are calculated by using all pixel points in the color range;
s5: calculating to obtain a similarity gray level map, wherein the similarity of each pixel point of the processed image is calculated according to the color characteristic parameters calculated in the step S4 to obtain the similarity gray level map;
s6: acquiring a communication region, wherein morphological open operation is carried out on the similarity gray level image, and a threshold range is set to acquire the communication region;
s7: and (3) acquiring a concentrate belt rectangular area, wherein the minimum circumscribed rectangle of the communication area in the step S6 is taken as the concentrate belt rectangular area.
Alternatively, in the method described above, in S4, parameters are set directly using the writing control.
Optionally, in the method as described above, the color characteristic parameter in S4 includes one or more of the following: tone center, tone upper boundary, tone lower boundary, saturation upper boundary, saturation lower boundary, luminance upper boundary, luminance lower boundary.
Alternatively, in the method as described above, in S4, the blurring process is a gaussian blurring process, the size of which is 5*5, and the scaling factor is 0.5.
Alternatively, in the method as described above, in S3, the range of the tone space is [0,30] u [330,360 ], the range of the saturation space is [0.08,1.0], and the range of the luminance space is [0.4,1.0].
The above-described technical solutions are only some portions of embodiments of the present application, and various embodiments including technical features of the present application can be derived and understood by those skilled in the art from the following detailed description of the present application.
It will be appreciated by persons skilled in the art that the effects that can be achieved by the present application are not limited to what has been particularly described hereinabove and other advantages of the present application will be more clearly understood from the following detailed description.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate embodiments of the application and together with the description serve to explain the principle of the application.
Fig. 1 is a schematic diagram of a method for cutting a shaking table ore belt based on three color elements according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments of the application, examples of which are illustrated in the accompanying drawings. The detailed description set forth below in connection with the appended drawings is intended as a description of exemplary embodiments of the present application and is not intended to represent the only embodiments in which the present application may be practiced. The following detailed description includes specific details in order to provide a thorough understanding of the application. It will be apparent, however, to one skilled in the art that the present application may be practiced without these specific details.
In some instances, well-known structures and devices are omitted or shown in block diagram form, focusing on important features of the structures and devices, so as not to obscure the concepts of the present application. The same reference numbers will be used throughout the specification to refer to the same or like parts.
The principles and features of the present application are described below with reference to the drawings, the examples are illustrated for the purpose of illustrating the application and are not to be construed as limiting the scope of the application.
In the description of the present application, it should be understood that the terms "upper," "lower," "center," "inner," "outer," "top," "bottom," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the application and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the application.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Fig. 1 shows a schematic diagram of a shake table ore belt segmentation method based on three color elements according to an embodiment of the present application. As shown in fig. 1, the application provides a shaking table ore belt segmentation method based on three color elements, which comprises the following steps: acquiring images of a shaking table ore belt, wherein the shaking table ore belt is photographed by an industrial camera, the acquired images are input into a computer, and the color space of the images is RGB space; preprocessing the image, wherein the acquired image is subjected to cutting, blurring and scaling to obtain a processed image; performing color space conversion, wherein the processed image is subjected to color space conversion from RGB space to tone, saturation, brightness space; acquiring color characteristic parameters of a shaking table concentrate belt, wherein a color range is set according to the color of the selected concentrate, all pixel points of the processed image are traversed, and the color characteristic parameters are calculated by using all pixel points in the color range; calculating to obtain a similarity gray level map, wherein the similarity of each pixel point of the processed image is calculated according to the color characteristic parameters calculated in the step S4 to obtain the similarity gray level map; acquiring a communication region, wherein morphological open operation is carried out on the similarity gray level image, and a threshold range is set to acquire the communication region; and (3) acquiring a concentrate belt rectangular area, wherein the minimum circumscribed rectangle of the communication area in the step S6 is taken as the concentrate belt rectangular area. It should be noted that: although the present embodiment abbreviations hue, saturation and brightness as H, S and I, the problem is not limited to the HSI color model. In fact, the solution provided by this embodiment is suitable for all existing and future color models where hue, saturation and brightness information of the colors are independent.
More specifically, the dividing process of the present embodiment is as follows:
the first step: and acquiring images of the shaking table ore belt, photographing the shaking table ore belt by using an industrial camera, inputting acquired image signals into a computer, and taking the color space of the images as RGB space.
And a second step of: preprocessing an Image, cutting, blurring and scaling the acquired Image to obtain a preprocessed Image, wherein Gaussian blur is adopted in the embodiment, the size is 5*5, the scaling multiple is 0.5, and two Gaussian blur and scaling operations are performed.
And a third step of: performing color space conversion on the Image, and converting the Image from RGB space into hue, saturation and brightness space HSI;
fourth step: obtaining color characteristic parameters of the shaking table concentrate belt, setting color range according to the color of the selected concentrate, wherein tin ore is selected in the embodiment, and the approximate range of the color tone H is [0,30]]U330,360, saturation S in the range of 0.08,1.0]The brightness I range is [0.4,1.0]]Traversing all pixels of an Image, conforming toThe pixel points of (a) form a set Ω, and the color feature parameters to be calculated and the calculation formula of this embodiment are as follows:
color center H center : maximum distance and minimum distance sum of hue H to hue 0 for all points within ΩHalf of (a) is provided.
Upper color tone boundary H Add : half of the sum of the absolute values of the maximum distance and the minimum distance from the hue H of all points in omega to the hue 0;
upper color tone boundary H Sub : half of the sum of the absolute values of the maximum distance and the minimum distance from the hue H of all points in omega to the hue 0;
upper hue boundary H AddLim ：min(H Add +5,30)；
Tone lower boundary H SubLim ：min(H Sub +5,30)；
Upper saturation limit S Add : saturation S maximum for all points within Ω;
saturation lower bound S Sub : saturation S minimum of all points in omega;
upper saturation boundary S AddLim ：min(S Add +0.01,1.0f)；
Lower saturation boundary S SubLim ：max(S Sub -0.01,0.0f)；
Luminance threshold I Threld : in this embodiment 0.43
Upper brightness boundary I Add : maximum value of brightness I of all points in Ω;
lower brightness boundary I Sub ：I Threld ；
Upper boundary of brightness I AddLim ：I Threld -0.05；
Lower boundary of brightness I SubLim ：min(I Add +0.05,0.94)；
Fifth step: according to the parameters determined in the fourth step, calculating the hue similarity alpha of the current pixel point (H, S, I) of the Image and the color of the concentrate belt H Saturation similarity alpha s Brightness similarity alpha I Obtaining similarity alpha, traversing all pixel points to obtain similarity of all points, and converting to [0,255 ]]And obtaining a similarity gray level diagram.
Wherein alpha in the present embodiment H ，α s ，α I The alpha calculation formula is as follows:
first, calculate the hues H to H of the current pixel point center Distance DisH of (2)
α H ＝f(H,DisH，H SubLim ,H Sub ,H AddLim ,H Add )
α S ＝f(S,S SubLim ,S Sub ,S AddLim ,S Add )
α I ＝f(I,I SubLim ,I Sub ,I AddLim ,I Add )
α＝α H ·α S ·α I
Sixth step: performing morphological opening operation on the similarity gray level graph, setting a threshold range, and acquiring a communication region with the threshold range of [25,255] in the embodiment;
seventh step: and (3) obtaining the minimum circumscribed rectangle of the communication area in the sixth step, namely the rectangular area of the concentrate which is required.
The application fully utilizes the information of brightness, tone and saturation of the image of the mine belt, is more in line with the model for distinguishing the mine belt by human eyes during manual operation, can well solve the difficult problems of water bubbles, light source reflection and the like, and has strong robustness.
From the above description of embodiments, it will be clear to a person skilled in the art that the present application may be implemented by means of software and necessary general purpose hardware, but may also be implemented by means of hardware. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the related art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk, or an optical disk of a computer, etc., and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present application.
As described above, a detailed description of the preferred embodiments of the present application has been given to enable those skilled in the art to make and practice the application. Although the present application has been described with reference to exemplary embodiments, those skilled in the art will appreciate that various modifications and changes can be made in the present application without departing from the spirit or scope of the present application as described in the appended claims. Thus, the present application should not be limited to the particular embodiments described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (5)
1. The method for cutting the rock bed ore belt based on the three color elements is characterized by comprising the following steps of:
s1: acquiring images of a shaking table ore belt, wherein the shaking table ore belt is photographed by an industrial camera, the acquired images are input into a computer, and the color space of the images is RGB space;
s2: preprocessing the image, wherein the acquired image is subjected to cutting, blurring and scaling to obtain a processed image;
s3: performing color space conversion, wherein the processed image is subjected to color space conversion from RGB space to tone, saturation, brightness space;
s4: acquiring color characteristic parameters of a shaking table concentrate belt, wherein a color range is set according to the color of the selected concentrate, all pixel points of the processed image are traversed, and the color characteristic parameters are calculated by using all pixel points in the color range;
s5: calculating to obtain a similarity gray level map, wherein the similarity of each pixel point of the processed image is calculated according to the color characteristic parameters calculated in the step S4 to obtain the similarity gray level map;
s6: acquiring a communication region, wherein morphological open operation is carried out on the similarity gray level image, and a threshold range is set to acquire the communication region;
s7: and (3) acquiring a concentrate belt rectangular area, wherein the minimum circumscribed rectangle of the communication area in the step S6 is taken as the concentrate belt rectangular area.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
in S4, parameters are set directly using the write control.
3. The method of claim 1, wherein the color characterization parameters in S4 include one or more of the following: tone center, tone upper boundary, tone lower boundary, saturation upper boundary, saturation lower boundary, luminance upper boundary, luminance lower boundary.
4. The method of claim 1, wherein the step of determining the position of the substrate comprises,
in S4, the blurring process is a gaussian blurring process, the size of which is 5*5, and the scaling factor is 0.5.
5. The method of claim 1, wherein the step of determining the position of the substrate comprises,
in S3, the range of the tone space is [0,30] U [330,360 ], the range of the saturation space is [0.08,1.0], and the range of the luminance space is [0.4,1.0].
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