CN107478656B - Paper pulp stirring effect detection and evaluation method, device and system based on machine vision - Google Patents
Paper pulp stirring effect detection and evaluation method, device and system based on machine vision Download PDFInfo
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Abstract
The invention relates to a paper pulp stirring effect detection and evaluation method and system based on machine vision, which comprises the following steps: the method comprises the following steps: acquiring a pulp image to be detected, wherein the pulp is in a stirred state; detecting a paper pulp fiber target in the paper pulp image to be detected by adopting an image edge detection method; and (3) processing the edges of the pulp fibers by adopting a pulp evenness detection method, and evaluating the stirring effect of the pulp. Compared with the prior art, the invention realizes online intelligent non-contact measurement, reduces the labor intensity, has accurate detection, improves the production efficiency, saves the labor force, is not influenced by human factors in the whole detection process, and effectively ensures the paper pulp quality.
Description
Technical Field
The invention belongs to the field of machine vision, and particularly relates to a paper pulp stirring effect detection and evaluation method and system based on machine vision.
Background
The information technology is widely applied in the paper making industry, and the development of the information technology greatly promotes and improves the automatic detection, automatic control and quality management levels, so that the paper making industry gradually distinguishes the era of low efficiency and slow return. Due to the application of computer image processing and analysis technology in the papermaking field, the papermaking industry has achieved unprecedented performance in both function and finished paper quality.
In the process of papermaking, the quality of the stirring effect of the paper pulp is directly related to the quality of finished paper, but at present, a detection system for detecting the stirring effect of the paper pulp on line does not exist, and the detection system can only be observed by human eyes and is time-consuming and labor-consuming.
The existing machine vision detection of paper pulp images belongs to the field of microcosmic, and mainly detects that morphological parameters of paper pulp fibers such as length, width, crimpness and the like under a microcosmic view angle have important influence on paper forming performance, but no detection and evaluation method for the stirring state in the paper pulp stirring process exists.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a paper pulp stirring effect detection and evaluation method based on machine vision, and non-contact and high-precision measurement of the paper pulp stirring effect is realized.
The invention adopts the following technical scheme:
a paper pulp stirring effect detection and evaluation method based on machine vision comprises the following steps:
acquiring a pulp image to be detected, wherein the pulp is in a stirred state;
detecting a paper pulp fiber target in the paper pulp image to be detected by adopting an image edge detection method;
and detecting the edge of a paper pulp fiber target by adopting a paper pulp evenness detection method, and evaluating the paper pulp stirring effect.
Further, converting the obtained pulp image to be detected into a gray image, and denoising the gray image.
Furthermore, aiming at the denoised gray level image, a minimum error threshold segmentation method is adopted to segment the fiber target in the paper pulp image.
Further, denoising the noisy gray image by adopting a wavelet transform method, which comprises the following specific steps:
selecting a set wavelet, determining a good decomposition level N, and performing N-layer wavelet decomposition on a noisy gray level image; selecting a set threshold function to process the wavelet coefficients of the N layers of wavelets based on non-noise signals and noise signals; and obtaining the wavelet coefficient containing the non-noise signal after the noise signal is removed, reconstructing the wavelet coefficient containing the non-noise signal, and obtaining the gray level image after the noise is removed.
Further, the minimum error threshold segmentation method adopts a minimum error objective function:
the optimum threshold t is obtained by the following formula:
further, the pulp evenness detection method is a gray scale space correlation characteristic detection method based on a gray scale co-occurrence matrix.
Further, in the gray level spatial correlation characteristic detection method based on the gray level co-occurrence matrix, the gray level co-occurrence matrix P is:
wherein f (x, y) is the pulp image to be segmented, S is the set of pixel pairs having a specific spatial relationship in the target region R;
the molecules on the right side of the equal sign of the above formula have specific spatial relationship and gray value of g respectively1And g2The denominator is the total number of the pixel pairs, # represents the number;
according to the formula
And obtaining the adverse moment of the pulp image, and evaluating the stirring effect of the pulp according to the adverse moment.
Further according to the formula
And obtaining the energy value of the pulp image, and evaluating the effect of pulp stirring according to the energy value.
The invention also provides an image processing device, which comprises a processor and a computer readable storage medium, wherein the processor is used for realizing the instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and to perform the process of:
detecting a paper pulp fiber target in a paper pulp image to be detected by adopting an image edge detection method;
and detecting the edge of a paper pulp fiber target by adopting a paper pulp evenness detection method, and evaluating the paper pulp stirring effect.
The invention also provides a paper pulp stirring effect detection and evaluation system based on machine vision, which comprises an industrial camera, a light source, an image acquisition device and an image processing device; the industrial camera and the light source are positioned in front of the pulp stirrer, and the view finding range of the industrial camera comprises pulp to be detected; the industrial camera is connected with an image acquisition device, and the image acquisition device is connected with an image processing device;
the image acquisition device comprises a first processor and a first computer readable storage medium, wherein the first processor is used for realizing instructions; a first computer readable storage medium for storing a plurality of instructions adapted to be loaded by a first processor and to perform a process of obtaining an image of pulp under test, the pulp being in a agitated state;
the image processing apparatus includes a second processor and a second computer-readable storage medium, the second processor to implement instructions; a second computer readable storage medium for storing a plurality of instructions adapted to be loaded by a second processor and to perform the following:
detecting a paper pulp fiber target in a paper pulp image to be detected by adopting an image edge detection method;
and detecting the edge of a paper pulp fiber target by adopting a paper pulp evenness detection method, and evaluating the paper pulp stirring effect.
Furthermore, the image acquisition device adopts an image acquisition card, and the image acquisition card performs A/D conversion on the acquired image and sends the converted image to the image processing device.
The invention has the beneficial effects that:
compared with the prior art, the invention realizes online intelligent non-contact measurement, reduces the labor intensity, has accurate detection, improves the production efficiency, saves the labor force, is not influenced by human factors in the whole detection process, and effectively ensures the paper pulp quality.
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FIG. 1 is a schematic diagram of a system architecture;
FIG. 2 is a flow chart of the wavelet threshold shrinkage denoising method;
FIG. 3 is a schematic diagram of the evaluation structure.
The specific implementation mode is as follows:
the invention will be further illustrated with reference to the following examples and drawings:
it should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
An exemplary embodiment of the invention is a pulp stirring effect detection and evaluation method based on machine vision:
acquiring a pulp image to be detected, wherein the pulp is in a stirred state;
detecting a paper pulp fiber target in the paper pulp image to be detected by adopting an image edge detection method;
and detecting the edge of a paper pulp fiber target by adopting a paper pulp evenness detection method, and evaluating the paper pulp stirring effect.
By adopting the method, the invention provides a paper pulp stirring effect detection and evaluation system based on machine vision, which comprises an industrial camera, a light source, an image acquisition device and an image processing device; the industrial camera and the light source are positioned in front of the pulp mixer, and the view finding range of the industrial camera comprises pulp to be measured; the industrial camera is connected with the image acquisition device, and the image acquisition device is connected with the image processing device.
The image acquisition device comprises a first processor and a first computer readable storage medium, wherein the first processor is used for realizing instructions; a first computer readable storage medium for storing a plurality of instructions adapted to be loaded by a first processor and to perform a process of obtaining an image of pulp under test, the pulp being in a agitated state;
the image processing apparatus includes a second processor and a second computer-readable storage medium, the second processor to implement the instructions; a second computer readable storage medium for storing a plurality of instructions adapted to be loaded by a second processor and to perform the following:
detecting a paper pulp fiber target in a paper pulp image to be detected by adopting an image edge detection method;
and detecting the edge of a paper pulp fiber target by adopting a paper pulp evenness detection method, and evaluating the paper pulp stirring effect.
Furthermore, the image acquisition device adopts an image acquisition card, and the image acquisition card performs A/D conversion on the acquired image and sends the converted image to the image processing device.
The image acquisition device adopts an image acquisition card, and the image acquisition card performs A/D conversion on the acquired image and sends the image to the image processing device.
The industrial camera adopts a CCD industrial camera; the light source adopts an LED light source.
The camera of the CCD industrial camera collects the image amplified by the high-power magnifier, and the image is subjected to A/D conversion by the image collecting card and converted into a digital image which can be processed by the image processing device; the image processing apparatus used in the present embodiment is generally a computer.
The CCD industrial camera obtains a paper pulp image to be detected in a stirred state, converts an original image into a gray image, de-noizes the gray image by a wavelet threshold shrinkage de-noising method, and then enhances the image by a linear image enhancement method, so that the definition of the paper pulp is integrally improved. And performing minimum error threshold segmentation on the enhanced pulp image to segment fibers in the pulp, and further researching the distribution condition of the fibers.
The specific steps of wavelet threshold shrinkage denoising are as follows:
(1) selecting a proper wavelet and determining a good decomposition level (N), and then performing N-layer wavelet decomposition on the two-dimensional image containing the noise; (2) selecting a proper threshold function to process wavelet coefficients generated by the image useful signals and the noise signals acquired in the step (1); (3) and (3) performing wavelet reconstruction processing on the residual wavelet coefficient after the processing in the step (2) to obtain a two-dimensional image with noise removed, wherein the residual wavelet coefficient is the noise signal with the high frequency part removed.
The method for partitioning the minimum error threshold comprises the following specific steps:
dividing pixels in the image into a background class and a target class C according to gray values by using a threshold value t0And C1I.e. C0＝{0,1,…,t}，C1T +1, t +2, …, L-1 (typically L256). Then C is0And C1The probability of each distribution is:
wherein p isiProbability of occurrence of a pixel point representing a gray value i in a gray image, ω0+ω1Let ω (t) be ω (1)0Then ω is1＝1-ω(t)。
C0And C1Mean value of respective distributions mu0And mu1Respectively as follows:
C0and C1Variance σ of respective distributions0 2And σ1 2Respectively as follows:
based on the minimum classification error idea, a minimum error objective function J (t) is given:
optimum threshold t*Obtained by the following formula:
based on the above formula, the fiber target in the pulp, i.e. the pulp fiber edge, is segmented.
Next, we used the pulp evenness test method to process the edges of the pulp fibers and evaluate the stirring effect of the pulp.
The pulp image acquisition window is set to 512 x 512 pixels, and the actual acquisition area is about 4cm2Carrying out background correction on the image to eliminate the uneven background light phenomenon; assuming f (x, y) as a pulp segmentation image and S as a set of pixel pairs having a specific spatial relationship in the target region R, the gray level co-occurrence matrix P satisfying a certain spatial relationship is:
the numerator on the right side of the equal sign of the above formula is the number of pixel pairs with a certain spatial relationship and gray values of g1 and g2, respectively, (x)1,y1) And (x)2,y2) The denominator is the total number of pixel pairs (# representative number) for the coordinates of the pixel points on the pulp-segmented image. According to formula (1)
And formula (2)
Obtaining the inverse moment and energy values of the pulp image, wherein i is g1J is g2. Judging the stirring effect of the paper pulp according to the inverse difference moment and the energy value, wherein the energy (ASM) reflects the uniformity degree of the gray level distribution of the image; the Inverse Difference Matrix (IDM) reflects the homogeneity of the image texture and measures how much the image texture changes locally. The larger the inverse difference moment and the energy are, the more uniform the pulp is stirred, the better the fiber is dispersed and distributed. The gray level co-occurrence matrix method is high in implementation speed, small in real-time calculation amount and good in detection effect.
Compared with the prior art, the embodiment realizes online intelligent non-contact measurement, reduces labor intensity, is accurate in detection, improves production efficiency, saves labor force, is not influenced by human factors in the whole detection process, and effectively ensures paper pulp quality.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (3)
1. A paper pulp stirring effect detection and evaluation method based on machine vision comprises the following steps: the method is characterized in that:
acquiring a pulp image to be detected, wherein the pulp is in a stirred state;
detecting a paper pulp fiber target in the paper pulp image to be detected by adopting an image edge detection method;
processing the edge of a paper pulp fiber target by adopting a paper pulp evenness detection method, and evaluating the paper pulp stirring effect; the pulp evenness detection method is a gray scale space correlation characteristic detection method based on a gray scale co-occurrence matrix;
converting the obtained pulp image to be detected into a gray image, and denoising the gray image;
segmenting a fiber target in the paper pulp image by adopting a minimum error threshold segmentation method aiming at the de-noised gray level image;
judging the stirring effect of the paper pulp according to the inverse difference moment and the energy value, wherein the energy value reflects the uniformity of the gray level distribution of the image; the adverse difference moment reflects the homogeneity of the image texture;
denoising the noisy gray image by adopting a wavelet transform method, which comprises the following specific steps: selecting a set wavelet function, determining a good decomposition level N, and performing N-layer wavelet decomposition on a noisy gray level image; selecting a set threshold function to process the wavelet coefficients of the N layers of wavelets based on non-noise signals and noise signals; obtaining a wavelet coefficient containing a non-noise signal after removing the noise signal, and reconstructing the wavelet coefficient containing the non-noise signal to obtain a gray level image after removing the noise;
the method for segmenting the fiber target in the pulp image by adopting the minimum error threshold segmentation method comprises the following steps: dividing pixels in the image into a background class and a target class C according to gray values by using a threshold value t0And C1I.e. C0＝{0,1,…,t}，C1T +1, t +2, …, L-1, then C0And C1The probability of each distribution is:
wherein p isiProbability of occurrence of a pixel point representing a gray value i in a gray image, ω0+ω1Let ω (t) be ω (1)0Then ω is1＝1-ω(t)，
C0And C1Mean value of respective distributions mu0And mu1Respectively as follows:
C0and C1Variance σ of respective distributions0 2And σ1 2Respectively as follows:
based on the minimum classification error idea, a minimum error objective function J (t) is given:
optimum threshold t*Obtained by the following formula:
the gray level co-occurrence matrix P in the gray level spatial correlation characteristic detection method based on the gray level co-occurrence matrix is as follows:
wherein f (x, y) is the pulp image to be segmented, S is the set of pixel pairs having a specific spatial relationship in the target region R;
the molecules on the right side of the equal sign of the above formula have specific spatial relationship and gray value of g respectively1And g2Of a pixel pairThe number, denominator is the total number of pixel pairs, # represents the number;
according to the formula
Obtaining the adverse moment of the pulp image, and evaluating the stirring effect of the pulp according to the adverse moment;
according to the formula
And obtaining an energy value of a pulp image, and evaluating the effect of pulp stirring according to the energy value, wherein i is g1, and j is g 2.
2. An image processing apparatus suitable for the pulp stirring effect detection and evaluation method according to claim 1, comprising a processor and a computer-readable storage medium, wherein the processor is used for realizing instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and to perform the process of:
detecting a paper pulp fiber target in a paper pulp image to be detected by adopting an image edge detection method;
and (3) processing the edge of the paper pulp fiber target by adopting a paper pulp evenness detection method, and evaluating the paper pulp stirring effect.
3. A machine vision-based pulp stirring effect detection and evaluation system suitable for the pulp stirring effect detection and evaluation method of claim 1, characterized in that: the system comprises an industrial camera, a light source, an image acquisition device and an image processing device; the industrial camera and the light source are positioned in front of the pulp stirrer, and the view finding range of the industrial camera comprises pulp to be detected; the industrial camera is connected with an image acquisition device, and the image acquisition device is connected with an image processing device;
the image acquisition device comprises a first processor and a first computer readable storage medium, wherein the first processor is used for realizing instructions; a first computer readable storage medium for storing a plurality of instructions adapted to be loaded by a first processor and to perform a process of obtaining an image of pulp under test, the pulp being in a agitated state;
the image processing apparatus includes a second processor and a second computer-readable storage medium, the second processor to implement instructions; a second computer readable storage medium for storing a plurality of instructions adapted to be loaded by a second processor and to perform the following:
detecting a paper pulp fiber target in a paper pulp image to be detected by adopting an image edge detection method;
and (3) processing the edge of the paper pulp fiber target by adopting a paper pulp evenness detection method, and evaluating the paper pulp stirring effect.
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| CN117212182B (en) * | 2023-11-07 | 2024-01-26 | 尚宝罗江苏节能科技股份有限公司 | Low-pulse pulp pump for papermaking pulp and detection method |
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