CN111563410B - Foam image movement speed detection processing method - Google Patents
Foam image movement speed detection processing method Download PDFInfo
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- CN111563410B CN111563410B CN202010228085.4A CN202010228085A CN111563410B CN 111563410 B CN111563410 B CN 111563410B CN 202010228085 A CN202010228085 A CN 202010228085A CN 111563410 B CN111563410 B CN 111563410B
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- 239000006260 foam Substances 0.000 title claims abstract description 95
- 238000001514 detection method Methods 0.000 title claims abstract description 19
- 238000003672 processing method Methods 0.000 title claims abstract description 11
- 238000000034 method Methods 0.000 claims abstract description 17
- 238000005259 measurement Methods 0.000 claims description 10
- 230000011218 segmentation Effects 0.000 claims description 5
- 238000005188 flotation Methods 0.000 abstract description 8
- 239000010865 sewage Substances 0.000 abstract description 8
- 229910052500 inorganic mineral Inorganic materials 0.000 abstract description 7
- 239000011707 mineral Substances 0.000 abstract description 7
- 230000002411 adverse Effects 0.000 abstract description 4
- 230000000694 effects Effects 0.000 abstract description 4
- 238000005336 cracking Methods 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000000149 argon plasma sintering Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000003708 edge detection Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P3/00—Measuring linear or angular speed; Measuring differences of linear or angular speeds
- G01P3/36—Devices characterised by the use of optical means, e.g. using infrared, visible, or ultraviolet light
- G01P3/38—Devices characterised by the use of optical means, e.g. using infrared, visible, or ultraviolet light using photographic means
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
- G06T7/251—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments involving models
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
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- 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
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Abstract
The invention provides a processing method for detecting the movement speed of a foam image, which is used for analyzing the movement modes of foam such as mineral flotation, sewage treatment and the like, extracting main characteristic information of the foam image, generalizing the foam image and detecting the movement speed of the foam through template matching. By generalizing the foam image, adverse effects on image matching caused by the conditions of foam deformation, foam rotation, foam combination, foam cracking and the like can be greatly reduced, the integral consistency of the foam image is maintained, the locality of the foam image is emphasized, the matching accuracy and the speed detection rate are improved, the detection accuracy of the foam motion speed is improved, and the automation degree of mineral flotation and sewage treatment processes is greatly improved by processing the foam image.
Description
Technical Field
The invention relates to the technical field of production detection in a flotation process, in particular to a processing method for detecting the motion speed of a foam image.
Background
In the process of generating a large amount of foam for mineral flotation, sewage treatment and the like, detecting the movement speed of the foam is an important parameter for measuring the process performance to control and regulate. However, the process still mainly depends on the naked eyes of experienced operators to observe and judge, the labor intensity is high, accurate detection is difficult to achieve, and no standard, standardized and measurable basis exists due to the difference of subjective judgment of the operators.
In the existing beneficiation and sewage treatment technology, because the detection method of manual observation is mainly adopted, time and labor are wasted, the detection result has large uncertainty and no unified measurement standard, and the working site environment is bad, so that adverse effects can be generated on the health and safety of operators.
Disclosure of Invention
The invention aims to provide a processing method for detecting the movement speed of a foam image, which can greatly promote the automation degree of related processes through analyzing and processing the foam image in the mineral flotation and sewage treatment processes, can improve the detection accuracy of the movement speed of the foam, further avoid subjective factors of operators and improve the working environment of operators.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a processing method for detecting the moving speed of a foam image specifically comprises the following steps:
s1, carrying out threshold segmentation on each acquired frame of foam image, and segmenting a foam dark area and a foam highlight area to obtain a highlight foam light spot area;
s2, defining a valley edge type pixel as a pixel point with a gray value smaller than the gray value of adjacent pixels at two sides along a certain direction, then comparing the pixel point with the neighborhood gray value according to the pixel point by scanning the foam image, extracting the valley edge type pixel, and taking the valley edge type pixel and the surrounding area thereof as an inter-foam edge area in the foam image;
s3, respectively endowing the acquired foam image with different values according to the difference of the type of each pixel point, and converting the acquired foam image into a generalized image with different values;
s4, extracting a target area from the generalization image obtained by converting the previous frame, and performing template matching in the generalization image obtained by converting the next frame;
s5, if the matching is successful, converting to obtain the movement speed of the foam image according to the coordinate difference corresponding to the target area in the front frame image and the rear frame image; if the matching fails, the motion speed obtained by the last detection is reserved.
Further, the type of each pixel in step S3 includes a light spot, an edge and others, where the light spot is a pixel in a highlighted foam light spot area, and the edge is a pixel in an inter-foam edge area.
Further, the process of template matching in step S4 includes the following steps:
s41, selecting a certain area in the generalized image of the previous frame as a template;
s42, sliding the selected template up and down and left and right in the next generalized image, and calculating the variance of the template and the corresponding area of the template to perform template matching;
s43, extracting two measurement values with the smallest variance in the template matching process, if the ratio of the two measurement values exceeds a certain set threshold, considering that the matching is successful, taking the measurement value with the smallest variance as a successful matching position, and if the ratio does not exceed the threshold, considering that the template matching is failed.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the motion modes of the foam such as mineral flotation, sewage treatment and the like are analyzed, the main characteristic information of the foam image is extracted, the foam image is generalized, and the motion speed of the foam is detected through template matching. By generalizing the foam image, adverse effects on image matching caused by the conditions of foam deformation, foam rotation, foam combination, foam cracking and the like can be greatly reduced, the integral consistency of the foam image is maintained, the locality of the foam image is emphasized, the matching accuracy and the speed detection rate are improved, the detection accuracy of the foam motion speed is improved, and the automation degree of mineral flotation and sewage treatment processes is greatly improved by processing the foam image.
Drawings
FIG. 1 is a flow chart of a method of processing foam image motion speed detection;
FIG. 2 is a schematic flow diagram of a pattern matching in the present invention;
FIG. 3 is a schematic diagram of a valley edge detection template defined in the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
The processing method for detecting the moving speed of the foam image, as shown in fig. 1, specifically comprises the following steps:
s1, carrying out threshold segmentation on each acquired frame of foam image, and segmenting a foam dark area and a foam highlight area to obtain a highlight foam light spot area;
in this embodiment, by installing the bracket and the camera at a proper distance right above the moving foam and configuring the light source, continuous shooting of foam images is performed, shielding is given to the housing around the light source, and light scattering is avoided. Each frame of foam image captured by the camera causes a highlight spot area on the top of the foam due to the light illumination, while other foam areas exhibit significantly lower brightness. The gray level difference of the two is obvious, and a segmentation threshold value can be obtained by adopting any method, so that the foam image is segmented into a foam dark area and a foam highlight area.
If the gray level is achieved, each frame of foam image is converted from an RGB image to a gray level image, then a proper morphological operation element is selected according to the size of a light spot, top cap operation is carried out on the gray level image, the foreground of the gray level image is extracted, and threshold segmentation is carried out on the foreground by using an OTSU method, so that a foam highlight light spot area can be obtained.
S2, defining a valley edge type pixel as a pixel point with a gray value smaller than the gray value of adjacent pixels at two sides along a certain direction, then comparing the pixel point with the neighborhood gray value according to the pixel point by scanning the foam image, extracting the valley edge type pixel, and taking the valley edge type pixel and the surrounding area thereof as an inter-foam edge area in the foam image;
in this embodiment, as shown in fig. 3, a boundary detection template X is defined, which is formed by 3*3 sub-templates Xm arranged in a square matrix, where X0 is located at the geometric center of the sub-templates, and each sub-template is formed by k×k pixels. Let f (i, j) denote the gray value of the original image pixel at point (i, j), g0 (i, j) denote the eigenvalue of f (i, j) in the horizontal direction at (i, j), g45 (i, j) denote the eigenvalue of f (i, j) in the 45 ° direction at (i, j), g90 (i, j) denote the eigenvalue in the 90 ° direction, and g135 (i, j) denote the eigenvalue in the 135 ° direction.
Represents k in m-th sub-template Xm* The average gray level of k pixels, P, is a given threshold. If the pixel mean value of the sub-template X0 +.>
Satisfy->
G0 (i, j) =1, otherwise g0 (i, j) =0; if it meets->
G45 (i, j) =1, otherwise g45 (i, j) =0; if it meets
G90 (i, j) =1, otherwise g90 (i, j) =0; if it meets->
G135 (i, j) =1, otherwise g135 (i, j) =0; g (i, j) = u [ g0 (i, j), g45 (i, j), g90 (i, j), g135 (i, j)]. The resulting g (i, j) is the extracted edge region, in this example k=5, p=2.
S3, respectively endowing the acquired foam image with different values according to different types of each pixel point, and converting the acquired foam image into a generalized image with different values, wherein the types of each pixel point comprise light spots, edges and others, the light spots are the pixel points in the highlighted foam light spot area, and the edges are the pixel points in the edge area between the foams.
In this embodiment, T (i, j) is the obtained generalized image, and when g (i, j) =1, T (i, j) =0, and when (i, j) belongs to the highlight spot region, T (i, j) =2, and T (i, j) =1 at the other (i, j).
S4, extracting a target area from the generalization image obtained by converting the previous frame, and performing template matching in the generalization image obtained by converting the next frame;
in this embodiment, let the length of the image be m and the width be n. Selecting a [ m/4:m x 3/4, n/4:n x 3/4] area in a previous frame of generalized image as a template, taking the template as T (x ', y'), sliding up and down and left and right in a next frame of generalized image I (x, y), and calculating variances of the two corresponding areas to match the template, wherein the calculation formula of the variances is as follows:
as shown in fig. 2, the process of template matching includes the steps of:
s41, selecting a certain area in the generalized image of the previous frame as a template;
s42, sliding the selected template up and down and left and right in the next generalized image, and calculating the variance of the template and the corresponding area of the template to perform template matching;
s43, extracting two measurement values with the smallest variance in the template matching process, if the ratio of the two measurement values exceeds a certain set threshold, considering that the matching is successful, taking the measurement value with the smallest variance as a successful matching position, and if the ratio does not exceed the threshold, considering that the template matching is failed.
In a specific implementation, R is selected diff_sq The first 2 local minima R1 and R2 in (1), if R2/R1>And P, considering that the matching is successful, wherein the coordinates (x, y) corresponding to R1 in the next frame of generalized image are the matching positions, and otherwise, considering that the matching is failed. Here, the threshold p=1.05 is taken.
S5, if the matching is successful, converting to obtain the movement speed of the foam image according to the coordinate difference corresponding to the target area in the front frame image and the rear frame image; if the matching fails, the motion speed obtained by the last detection is reserved.
The processing method provided by the invention emphasizes the regional characteristics of the foam image more, does not take a single pixel point as a characteristic, emphasizes the locality of the foam image while maintaining the integral consistency of the foam image, can greatly lighten the adverse effects of conditions such as foam deformation, foam rotation, foam merging, foam cracking and the like on image matching, improves the matching accuracy and the speed detection rate, improves the detection accuracy of the foam movement speed, and has guiding significance on automatic detection and optimization control of production processes such as mineral flotation, sewage treatment and the like.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (3)
1. The processing method for detecting the moving speed of the foam image is characterized by comprising the following steps of:
s1, carrying out threshold segmentation on each acquired frame of foam image, and segmenting a foam dark area and a foam highlight area to obtain a highlight foam light spot area;
s2, defining a valley edge type pixel as a pixel point with a gray value smaller than the gray value of adjacent pixels at two sides along a certain direction, then comparing the pixel point with the neighborhood gray value according to the pixel point by scanning the foam image, extracting the valley edge type pixel, and taking the valley edge type pixel and the surrounding area thereof as an inter-foam edge area in the foam image;
s3, respectively endowing the acquired foam image with different values according to the difference of the type of each pixel point, and converting the acquired foam image into a generalized image with different values;
s4, extracting a target area from the generalization image obtained by converting the previous frame, and performing template matching in the generalization image obtained by converting the next frame;
s5, if the matching is successful, converting to obtain the movement speed of the foam image according to the coordinate difference corresponding to the target area in the front frame image and the rear frame image; if the matching fails, the motion speed obtained by the last detection is reserved.
2. The processing method for detecting the moving speed of a foam image according to claim 1, wherein: the type of each pixel in step S3 includes a light spot, an edge and others, where the light spot is a pixel in a highlighted foam light spot area, and the edge is a pixel in an inter-foam edge area.
3. The processing method for detecting the moving speed of a foam image according to claim 1, wherein: the process of template matching in the step S4 comprises the following steps:
s41, selecting a certain area in the generalized image of the previous frame as a template;
s42, sliding the selected template up and down and left and right in the next generalized image, and calculating the variance of the template and the corresponding area of the template to perform template matching;
s43, extracting two measurement values with the smallest variance in the template matching process, if the ratio of the two measurement values exceeds a certain set threshold, considering that the matching is successful, taking the measurement value with the smallest variance as a successful matching position, and if the ratio does not exceed the threshold, considering that the template matching is failed.
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| CN115294379B (en) * | 2022-09-29 | 2023-01-03 | 南通甘雨钢化玻璃制品有限公司 | Flotation method foam identification method based on optical information |
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| CN104050687A (en) * | 2014-06-26 | 2014-09-17 | 中国矿业大学(北京) | Analyzing and processing method for flotation bubble motion pattern |
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| CN104050687A (en) * | 2014-06-26 | 2014-09-17 | 中国矿业大学(北京) | Analyzing and processing method for flotation bubble motion pattern |
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| 任会峰 ; 阳春华 ; 周璇 ; 桂卫华 ; 鄢锋 ; .基于自适应谷底检测的浮选泡沫形态特征提取.化工自动化及仪表.2011,(07),全文. * |
| 刘颖 ; 张平 ; 赵 ; 吕政 ; 王伟 ; .基于尺度不变特征变换的浮选泡沫图像动态特性提取方法.控制理论与应用.2016,(06),全文. * |
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