CN111982923A - Paper defect detection driving power supply optimization method based on stroboscopic imaging principle - Google Patents
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Abstract
The invention discloses a paper defect detection driving power supply optimization method based on a stroboscopic imaging principle, which relates to the technical field of paper defect detection and comprises the following steps: calculating the sampling frequency of the camera, determining the initial frequency of the light source according to the sampling frequency of the camera, and controlling the light source to work according to the initial frequency; converting the moving paper into a digital image using a camera; analyzing the image to obtain a gray average value and a gray uniformity; and judging whether the gray level mean value and/or the gray level uniformity reach the set level, if not, increasing the multiple of the sampling frequency of the camera to the light source frequency, and continuing image acquisition and analysis until the gray level mean value and/or the gray level uniformity of the image reach the set level. The method can effectively improve the image acquisition effect of the high-speed moving paper and lay a good foundation for the detection and identification process of the later-stage paper defects.
Description
Technical Field
The invention relates to the technical field of paper defect detection, in particular to a paper defect detection driving power optimization method based on a stroboscopic imaging principle.
Background
A paper defect detection system based on machine vision is a emerging surface defect detection technology developed recently, and compared with the traditional paper defect detection technology, the paper defect detection system based on machine vision has the characteristics of non-contact, high precision and high efficiency. With the continuous improvement of the speed of the paper machine, enterprises have more and more requirements on paper defect detection systems based on machine vision, a plurality of factories and merchants at home and abroad propose corresponding solutions, and the paper defect detection systems based on the machine vision are paid more and more attention by research institutions.
In the paper defect detection process, the paper running speed can reach 2800m/min at the fastest speed, and the detection precision requirement reaches 0.3m2The frequency of the linear CCD camera needs to reach 156 kHz to meet the requirements of the system, the requirement of the acquisition system on the light source is very high under the condition, even small brightness fluctuation can cause the change of image pixels, the image acquired by the current system has a relatively regular transverse ripple phenomenon as shown in figure 1(a), the image gray level uniformity is only 83.6%, great interference is brought to later-stage image analysis, and the execution efficiency of the paper defect detection system is reduced.
Disclosure of Invention
The invention provides a paper defect detection driving power supply optimization method based on a stroboscopic imaging principle, which can solve the problems in the prior art.
The invention provides a paper defect detection driving power optimization method based on a stroboscopic imaging principle, which comprises the following steps:
step 2, image acquisition: converting the moving paper into a digital image using a camera;
step 3, image analysis: analyzing the image to obtain a gray average value and a gray uniformity;
step 4, frequency adjustment: and judging whether the gray level mean value and/or the gray level uniformity reach the set level, if not, increasing the multiple of the sampling frequency of the camera to the light source frequency, and continuing image acquisition and analysis until the gray level mean value and/or the gray level uniformity of the image reach the set level.
The paper defect detection driving power supply optimization method based on the stroboscopic imaging principle has the beneficial effects that:
the image acquisition effect of the high-speed moving paper is effectively improved, and a good foundation is laid for the detection and identification process of the later-stage paper defects.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a prior art image of a sheet and a light source acquired;
FIG. 2 is a graph of pixel plots of a prior art sheet image and a light source image in the machine direction;
FIG. 3 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Before the method of the present invention is described, it is necessary to analyze the cause of the occurrence of the lateral waviness in the prior art paper and light source images.
The shannon sampling theorem is an important basic conclusion in the communication and signal processing discipline and is an important basis for discretizing continuous signals. Shannon sampling theorem states that: in order to recover the waveform of the collected signal without distortion, the sampling frequency of the collecting system should be at least 2 times greater than the highest frequency of the collected signal, and the higher the sampling rate of the collecting system is, the closer the waveform recovered in the future is to the collected signal.
In the paper defect detection system, the process of changing moving paper into a digital image by using a linear CCD camera accords with the acquisition process of Shannon sampling theorem, and regular ripples appearing in the image can show that the regular change of certain signals in the external environment is acquired in the image acquisition process. In order to determine the category of the signal, the invention analyzes two elements of paper and a light source which may affect the acquisition effect by using an elimination method, and finally acquires a light source image as shown in fig. 1(b) after removing the paper, wherein the light source image has higher similarity on the frequency of ripples with that of fig. 1 (a). In order to better compare the two images, the invention analyzes the gray values of the pixels of the images at the same column coordinate positions, and the result is shown in fig. 2.
The gray scale change rule of fig. 2(a) is not obvious due to the influence of the uneven density of the paper, and the curve graph of the light source of fig. 2(b) after the paper is removed is smoother and the change rule is more obvious. The frequency f of the light source is shown in formula (1):
in the formula: n is the number of occurrences of the periodic signal, and T is the time taken for the periodic signal to occur for these numbers. From fig. 2, it can be seen that approximately 8.5 cycles occur at a distance of 231 pixels, the current acquisition frequency is 2700Hz, the time elapsed for 231 pixels is 231/2700 ≈ 0.085 seconds, and the frequency of the light source obtained by formula (1) is approximately 100 Hz. The light source frequency is far less than the acquisition frequency, and accords with the Shannon sampling theorem. It follows that in a paper defect detection system, the regular transverse ripples are due to the system's acquisition frequency being higher than the frequency of the light source.
Referring to fig. 3, the present invention provides a paper defect detection driving power optimization method based on a stroboscopic imaging principle, which includes the following steps:
and step 1, frequency synchronization. This step comprises the following two substeps:
step 1.1, receiving two parameters of paper machine running speed and detection precision input from outside, and determining the sampling frequency of a linear CCD camera according to the two parameters, as follows:
in the formula (f)cThe sampling frequency of the camera is Hz, V is the running speed of the paper machine, m/S and S is the detection precision, mm2. The sampling frequency obtained by calculation of the formula (2) can ensure that the length and the width of the pixel acquired by the CCD camera at each time are equal, and further ensure that the acquired image cannot be distorted.
Step 1.2, according to a stroboscopic imaging principle, setting an initial value of a positive integer a to be 1 according to the following formula, and obtaining an initial frequency f of a light sourcelInitial frequency f to be determinedlTransmitting the signal to a light source control chip to enable the light source to work according to a set frequency:
and 2, acquiring an image. The moving paper is converted into a digital image by using a linear CCD camera, and then the acquired image is uploaded to an upper computer.
And 3, storing the image. And storing the acquired images to a local hard disk in a lossless and real-time manner, so as to provide effective data for the next image analysis.
In this embodiment, in order to ensure the authenticity of the image, a lossless gray BMP format is used during image storage, and the format is not compressed at all during storage, so that the image information can be completely stored. Meanwhile, the gray BMP format only needs to use one channel to represent data information during storage, so that compared with a BMP image in an RGB format using three channels, the storage space can be effectively saved, and the data processing speed is increased.
And 4, analyzing the image. The step comprises analyzing two parameters of the average value of pixel gray scale of the image and the uniformity of the gray scale of the image.
The pixel gray level mean value of the image is obtained by adding all the pixel gray levels and dividing the sum by the total number of pixels, as follows:
in the formula, PaveMean value of pixel gray levels, P, representing an imageiIs the gray scale value of the ith pixel and N is the total number of pixels in the image. The higher the gray average value is, the higher the image brightness is, the lower the gray average value is, the lower the image brightness is, and the over-high or under-low image brightness affects the judgment and identification of the paper defect type by the system. The average value of the gray scale of the image is generally required to be in a range of 100 to 160, so that the effect is closest to the effect observed by human eyes, and the detection effect of the paper defects is favorably improved.
The gray level uniformity of an image mainly reflects the uniformity and uniformity of the gray level in an image, and is usually analyzed in a standard deviation manner. The standard deviation can better reflect the dispersion degree of the gray value of the image. The content to be analyzed by the invention is the non-uniformity of the gray scale in the image longitudinal direction, the change is not large in the transverse direction, and the problem of image gray scale value deviation is easily caused when the density of the paper is not uniform, so the standard deviation method is not suitable for the analysis.
The invention combines the calculation method of the illumination uniformity to divide the analysis process of the gray level uniformity into the following two steps:
firstly, the average value of each line of pixels is obtained according to the line unit in the image, and the formula (5) is shown as follows:
in the formula, PmIs the average value of the m-th row of pixels, PmnIs the gray scale value of the n pixels of the mth row and the M is the total number of the pixels of the mth row.
Then, the minimum value and the maximum value of all the line average values are found, and then the minimum line average value is divided by the maximum line average value to obtain the final gray level uniformity, as shown in formula (6):
Grate＝min(Pm)/max(Pm) (6)
in the formula, GrateIndicating gray scale uniformity, min indicating a minimum function, and max indicating a maximum function. G in the formularateThe larger the value is, the better the gray level uniformity effect is, and after the gray level of the image is uniform in an ideal state, the result should be 1, and in the paper defect detection, since the gray level of the image is also affected by the paper density, the gray level uniformity of the paper image is only close to 1 and is not equal to 1.
And 5, adjusting the frequency. And judging whether the gray average value and/or the gray uniformity obtained by image analysis reach a set level, and if so, controlling the light source to work by adopting the determined light source frequency. Otherwise, increasing the positive integer a according to the set increment to increase the light source frequency flSampling frequency f for cameracAnd (4) continuing to perform image acquisition and analysis work until the gray level mean value and/or the gray level uniformity of the image reach a set level.
In this embodiment, the level set for the gray level average is that the gray level average is between 100 and 160, and the level set for the gray level uniformity is that the gray level uniformity reaches more than 98.5%.
Description of the experiments
The mode of setting the aperture and the focal length is adopted in the experimental design, and the acquisition frequency of the CCD camera and the driving frequency of the LED driving power supply are mainly changed in the experiment. In the experimental process, the stability of a light source is mainly reflected by different multiplying power settings, meanwhile, the gray average value of image pixels is considered, and in order to reflect the relation between the gray uniformity and the acquisition frequency, experiments among four multiplying frequencies of 3 times, 6 times, 9 times and 12 times are designed for comparison.
From the experimental result, when the multiple is 3, the uniformity of the image gray scale collected by the system is obviously jittered and the stability is poor, when the collection frequency is low, the average value of the image gray scale is higher than the ideal gray scale set observed by human eyes, and when the collection frequency is higher than 10kHz, the average value of the image gray scale is lower than the ideal set observed by human eyes.
The experiment comprehensively considers the environmental factors such as the temperature, the humidity and the like of the paper production workshop, and in order to enable the experimental equipment to have higher combination degree with the production environment, the experimental equipment is changed as little as possible when being transplanted into the production environment. The customized white light 5054 lamp bead of Shenzhen Planckian photoelectricity technology Limited company is used in design of an experimental lamp, and has the advantages of being waterproof and dustproof, uniform in luminance, large in adjustable range of luminous intensity, high in luminous flux and capable of reaching 130lm and the like. In the aspect of an LED driving power supply, a Texas instrument LM3421 is adopted as a lamp bead control chip, the chip is mainly used for a constant-current LED driver, a high-voltage starting voltage stabilizer is contained in the chip to strictly stabilize the current of an LED, the stability of luminous flux emitted by a lamp bead can be effectively controlled, programmable switching frequency up to 2MHz can be realized by the chip to the maximum extent, and the requirement of the invention is met. In the aspect of image acquisition, a Charge-coupled Device (CCD) camera of a spray 3 series manufactured by TELEDYNE DALSA company is used, the model is S3-24-04k40, the camera can maximally realize 4096 multiplied by 2 double-line scanning resolution, the maximum line frequency is 18.5kHz, and the accuracy is 0.5mm2The detection speed of 550m per minute can be realized at the fastest speed under the condition, the lens adopts an AF NIKKOR lens of Nikon, the model is AF 20mm F/2.8D, the lens is a fixed focus lens with an F2.8 large aperture, and the requirement of an experiment on light flux adjustment can be met.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (7)
1. A paper defect detection driving power supply optimization method based on a stroboscopic imaging principle is characterized by comprising the following steps:
step 1, frequency synchronization: calculating the sampling frequency of the camera, determining the initial frequency of the light source according to the sampling frequency of the camera, and controlling the light source to work according to the initial frequency;
step 2, image acquisition: converting the moving paper into a digital image using a camera;
step 3, image analysis: analyzing the image to obtain a gray average value and a gray uniformity;
step 4, frequency adjustment: and judging whether the gray level mean value and/or the gray level uniformity reach the set level, if not, increasing the multiple of the light source frequency to the camera sampling frequency, and continuing image acquisition and analysis until the gray level mean value and/or the gray level uniformity of the image reach the set level.
2. The paper defect detection driving power optimization method based on the stroboscopic imaging principle as claimed in claim 1, wherein step 1 specifically comprises:
step 1.1, receiving input running speed and detection precision of the paper machine, and determining sampling frequency of a camera according to the two parameters, wherein the sampling frequency is as follows:
in the formula (f)cThe sampling frequency of a camera, V is the running speed of a paper machine, and S is the detection precision;
step 1.2, according to a stroboscopic imaging principle, setting an initial value of a positive integer a to be 1 according to the following formula, and obtaining an initial frequency f of a light sourcelAnd transmitting the determined initial frequency to a light source control chip so that the light source works according to the set frequency:
3. the method for optimizing paper defect detection driving power supply based on stroboscopic imaging principle as claimed in claim 1, wherein in step 3, the gray level mean value of the image is calculated by the following formula:
in the formula, PaveRepresenting the mean value of the gray levels of the image, PiIs the gray scale value of the ith pixel and N is the total number of pixels in the image.
4. The method for optimizing paper defect detection driving power supply based on stroboscopic imaging principle as claimed in claim 1, wherein the method for calculating the gray scale uniformity of the image in step 3 is:
firstly, the average value of each line of pixels is obtained according to the line unit in the image, and the following formula is shown:
in the formula, PmIs the average value of the m-th row of pixels, PmnThe gray value of the n columns of pixels on the mth row, and M is the total number of the pixels on the mth row;
then find the minimum and maximum values of all the line averages, and then divide the minimum line average by the maximum line average to obtain the final gray uniformity, as follows:
Grate＝min(Pm)/max(Pm)
in the formula, GrateIndicating gray scale uniformity, min indicating a minimum function, and max indicating a maximum function.
5. The method as claimed in claim 1, wherein the average gray level is set to a level between 100 and 160, and the uniformity gray level is set to a level above 98.5%.
6. The method of claim 1, wherein the acquired image is stored after the image acquisition is completed.
7. The method of claim 6, wherein the image is stored in a gray BMP format.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113504241A (en) * | 2021-07-13 | 2021-10-15 | 陕西科技大学 | Design method of paper defect detection system |
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