CN110717899A - Online visual detection method for bead explosion defects of transparent filter rod - Google Patents
Online visual detection method for bead explosion defects of transparent filter rod Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 42
- 239000011324 bead Substances 0.000 title claims abstract description 35
- 238000004880 explosion Methods 0.000 title claims abstract description 27
- 230000000007 visual effect Effects 0.000 title claims abstract description 16
- 238000001914 filtration Methods 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims abstract description 4
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 238000009826 distribution Methods 0.000 claims description 9
- 238000011179 visual inspection Methods 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000000265 homogenisation Methods 0.000 claims description 3
- 238000003706 image smoothing Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 17
- 235000019504 cigarettes Nutrition 0.000 abstract description 4
- 241000208125 Nicotiana Species 0.000 abstract description 2
- 235000002637 Nicotiana tabacum Nutrition 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 description 12
- 230000002950 deficient Effects 0.000 description 5
- 238000000034 method Methods 0.000 description 5
- 238000005070 sampling Methods 0.000 description 4
- 230000018109 developmental process Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000003384 imaging method Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000000875 corresponding Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000005422 blasting Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000011089 mechanical engineering Methods 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006011 modification reaction Methods 0.000 description 1
- 230000003287 optical Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000004382 visual function Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
- G01N21/958—Inspecting transparent materials or objects, e.g. windscreens
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/001—Image restoration
- G06T5/002—Denoising; Smoothing
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- G06T5/00—Image enhancement or restoration
- G06T5/40—Image enhancement or restoration by the use of histogram techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/90—Determination of colour characteristics
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
- G06T2207/20032—Median filtering
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
Abstract
The invention belongs to the technical field of online visual detection methods, and particularly relates to an online visual detection method for bead explosion defects of a transparent filter rod. The method comprises the following steps: image preprocessing, specifically, converting a transparent filter stick image picture into a black and white image f (x, y), wherein f (x, y) is a gray value of a coordinate (x, y) point in the image; constructing a gray level histogram, wherein the abscissa axis in the gray level histogram represents a gray level value, the range of the gray level value is 0-255, the ordinate axis represents the proportion of the pixel number of a certain gray level to the pixel number of the whole image, namely the occurrence frequency of the gray level, a set variable r represents the gray level of the pixel in the image, the gray level of the pixel is normalized, and the gray level is adjusted: for each pixel point (m, n) of a given image f (i, j), taking the neighborhood S as a 3 x 3 matrix, locating the point (m, n) at the center of the image, obtaining a noise-containing image f, carrying out neighborhood averaging and then carrying out median filtering; the method has the advantages of fast and accurate defects, is beneficial to improving the production rate and the production quality of the cigarette filter sticks, and creates economic benefits and social benefits for tobacco companies.
Description
Technical Field
The invention belongs to the technical field of online visual detection methods, and particularly relates to an online visual detection method for bead explosion defects of a transparent filter rod.
Background
Defective products with quality defects, such as more beads, less beads, phase deviation and the like, can be generated in the production process of the transparent filter stick, the problems of inaccurate length, wrong arrangement, splicing gaps, material mixing, bead existence, bead breakage and the like exist in the transparent filter stick process, and screening and removing the defective filter stick products are important production links for improving the quality of cigarette filter stick products. The traditional filter stick defective product detection uses a manual sampling method, but the manual sampling detection has the defects of low detection speed, high detection error rate and the like, so that the requirements of high-speed and accurate detection cannot be met at present. At present, the domestic common automatic detection and removal methods for defective filter sticks comprise a sensor detection method and a microwave detection method, the detection speed and the detection effect of the detection methods are improved compared with a manual sampling method, but the detection methods have the defects of higher equipment cost and poorer detection effect, so that the detection speed of transparent filter stick products needs to be further improved, and the false alarm rate of defective filter sticks needs to be reduced.
With the continuous development and progress of machine vision hardware technology and software technology, the weight of machine vision technology on the development of manufacturing industry is also continuously increased. Machine vision is a technique for automatically detecting and judging industrial products by machines. Machine vision mainly uses a computer to simulate the visual function of a human, extracts information from an image of an objective object, processes and understands the information, and finally is used for actual detection, measurement, control and identification. It is a comprehensive technology, including digital image processing technology, mechanical engineering technology, control technology, light source lighting technology, optical imaging technology, sensor technology, analog and digital video technology, computer software and hardware technology, man-machine interface technology, etc. The machine vision emphasizes the practicability, can adapt to the severe environment of an industrial field, and has reasonable cost performance, universal industrial interface, higher fault-tolerant capability and safety, stronger universality and transportability; more emphasizes real-time performance, and requires high speed and high precision. The machine vision system is characterized in that a machine vision product converts a shot target into image signals and transmits the image signals to a corresponding image processing module, the image processing module performs various operations on the signals and extracts the characteristics of the target, and then the field equipment action is controlled according to the extracted characteristic judgment result. Compared with other detection methods, the transparent filter stick bead explosion defect online visual detection method has higher detection speed and accuracy, so that the production efficiency and the production automation degree can be greatly improved. Meanwhile, the machine vision is easy to integrate and process information, and an image processing module of the machine vision can be easily expanded and improved to adapt to the change of the product detection requirement, so that a machine vision system is widely applied to various fields such as industrial product detection and the like in the modern automatic production process. The machine vision in the cigarette manufacturing industry is more important for the production and detection of the bead-blasting filter stick.
Disclosure of Invention
The invention aims to provide a transparent filter stick bead explosion defect online visual detection method which is rapid and accurate, is beneficial to improving the production rate and the production quality of cigarette filter sticks and creates economic benefits and social benefits for tobacco companies.
In order to achieve the purpose, the invention adopts the following technical scheme.
An on-line visual inspection method for the bead explosion defect of a transparent filter stick comprises the following steps:
image preprocessing, specifically, converting a transparent filter stick image picture into a black and white image f (x, y), wherein f (x, y) is a gray value of a coordinate (x, y) point in the image;
constructing a gray level histogram, wherein the abscissa axis representsThe gray value ranges from 0 to 255, the ordinate represents the proportion of the number of pixels of a certain gray level to the number of pixels of the whole image, namely the occurrence frequency of the gray level, a variable r represents the gray level of the pixels in the image, the gray value of the pixels is normalized, and the range of r is as follows: r is not less than 0<1, r-0 for black and r-1 for white, each pixel takes [0,1]The gray level in the interval is constructed by representing the gray distribution density curve of the original image by a probability density function P (r), and replacing the density function P of the gray distribution by the histogram of the image f (x, y)f(f) Obtaining the image after histogram homogenization
Adjusting the gray scale: the gray scale range for acquiring the original image f (x, y) is [ M, M]The preset adjusted gray scale range of the image g (x, y) is [ N, N ]]，Image smoothing, specifically, for each pixel point (m, n) of a given image f (i, j), taking a neighborhood S of the pixel point as a 3 × 3 matrix, and locating the pixel point (m, n) at the center of the image to obtain a noise-containing image f after neighborhood averaging:and performing median filtering processing on the image.
The further improvement of the transparent filter stick bead explosion defect online visual detection method also comprises the following steps,
the method comprises the steps of bead explosion detection, namely converting a three-channel image into three single-channel images, respectively selecting the component distribution of the single-channel images r, g and b after conversion, selecting bead explosion within a gray range from the three single channels, solving a union set between two areas, calculating the parts connected with the areas, selecting a circular part, and counting the number of bead explosion.
The further improvement of the transparent filter stick bead explosion defect online visual detection method also comprises the steps of dividing the intensity of each color into 256 levels based on RGB images, wherein the intensity is from 0 to 255, 255 represents the most saturated state, 0 represents that the light intensity is 0 and means that no light exists, and converting the image from the RGB color space to the hsv color space; then, the detection range is narrowed according to the saturation and the chroma information; and performing color segmentation on the image according to the value range of a certain color in the H channel to obtain the color defect of the exploded beads of the transparent filter stick.
The further improvement of the method for the online visual detection of the bead explosion defect of the transparent filter stick also comprises the step of carrying out digital image pairDiscrete approximation is made to obtain clearer image details and more proportionally balanced gray levels.
The beneficial effects are that: the transparent filter stick bead explosion defect online visual detection method can greatly improve the production efficiency and the production automation degree of products while greatly reducing the labor cost, and has the following advantages compared with the traditional method:
(1) objectivity: the result obtained by corresponding calculation according to the sampling image does not need manual intervention in the whole process, so the obtained result is objective and reliable;
(2) the accuracy is as follows: because the precision of the imaging equipment can reach thousandth of an inch, the micro change of the product can be detected, and a more accurate result can be obtained through reasonable calculation;
(3) the speed is high: high-speed imaging equipment and an optimized algorithm can be adopted to meet the requirement of a high-speed production line;
(4) the cost is low: the machine vision method can continuously and efficiently work so as to reduce the labor cost of product production, and along with the continuous progress of technologies such as artificial intelligence, machine learning and the like and the continuous development of computer hardware and software technologies, the machine vision method can also be continuously developed and advanced, so that more and more advantages are obtained in the manufacturing industry.
Detailed Description
The invention is described in detail below with reference to specific embodiments.
An on-line visual inspection method for the bead explosion defect of a transparent filter stick comprises the following steps:
image preprocessing, specifically, converting a transparent filter stick image picture into a black and white image f (x, y), wherein f (x, y) is a gray value of a coordinate (x, y) point in the image;
constructing a gray level histogram, wherein the abscissa axis in the gray level histogram represents a gray level value, the range of the gray level value is 0-255, the ordinate axis represents the proportion of the pixel number of a certain gray level to the pixel number of the whole image, namely the occurrence frequency of the gray level, a set variable r represents the gray level of the pixel in the image, the gray level of the pixel is normalized, and the range of r is as follows: r is not less than 0<1, r-0 for black and r-1 for white, each pixel takes [0,1]The gray level in the interval is constructed by representing the gray distribution density curve of the original image by a probability density function P (r), and replacing the density function P of the gray distribution by the histogram of the image f (x, y)f(f) Obtaining the image after histogram homogenization
Adjusting the gray scale: the gray scale range for acquiring the original image f (x, y) is [ M, M]The preset adjusted gray scale range of the image g (x, y) is [ N, N ]]，Image smoothing, specifically, for each pixel point (m, n) of a given image f (i, j), taking a neighborhood S of the pixel point as a 3 × 3 matrix, and locating the pixel point (m, n) at the center of the image to obtain a noise-containing image f after neighborhood averaging:and performing median filtering processing on the image.
The further improvement of the transparent filter stick bead explosion defect online visual detection method also comprises the following steps,
the method comprises the steps of bead explosion detection, namely converting a three-channel image into three single-channel images, respectively selecting the component distribution of the single-channel images r, g and b after conversion, selecting bead explosion within a gray range from the three single channels, solving a union set between two areas, calculating the parts connected with the areas, selecting a circular part, and counting the number of bead explosion.
The further improvement of the transparent filter stick bead explosion defect online visual detection method also comprises the steps of dividing the intensity of each color into 256 levels based on RGB images, wherein the intensity is from 0 to 255, 255 represents the most saturated state, 0 represents that the light intensity is 0 and means that no light exists, and converting the image from the RGB color space to the hsv color space; then, the detection range is narrowed according to the saturation and the chroma information; and performing color segmentation on the image according to the value range of a certain color in the H channel to obtain the color defect of the exploded beads of the transparent filter stick.
The further improvement of the method for the online visual detection of the bead explosion defect of the transparent filter stick also comprises the step of carrying out digital image pairDiscrete approximation is made to obtain clearer image details and more proportionally balanced gray levels.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (4)
1. The method for online visual detection of the bead explosion defect of the transparent filter stick is characterized by comprising the following steps of:
step one, image preprocessing, specifically, converting a transparent filter stick image picture into a black and white image f (x, y), wherein f (x, y) is a gray value of a coordinate (x, y) point in the image;
constructing a gray level histogram, wherein the abscissa axis in the gray level histogram represents a gray level value, the range of the gray level value is 0-255, the ordinate axis represents the proportion of the pixel number of a certain gray level to the pixel number of the whole image, namely the occurrence frequency of the gray level, a set variable r represents the gray level of the pixel in the image, the gray level of the pixel is normalized, and the range of r is as follows: r is not less than 0<1, r-0 for black and r-1 for white, each pixel being taken[0,1]The gray level in the interval is constructed by representing the gray distribution density curve of the original image by a probability density function P (r), and replacing the density function P of the gray distribution by the histogram of the image f (x, y)f(f) Obtaining the image after histogram homogenization
Adjusting the gray scale: the gray scale range for acquiring the original image f (x, y) is [ M, M]The preset adjusted gray scale range of the image g (x, y) is [ N, N ]]，
Step two, image smoothing, specifically, for each pixel point (m, n) of a given image f (i, j), taking the neighborhood S as a 3 × 3 matrix, and the point (m, n) is located at the center of the image, so as to obtain the noise-containing image f after neighborhood averaging:
and performing median filtering processing on the image.
2. The on-line visual inspection method for the bead explosion defect of the transparent filter stick according to claim 1,
the method comprises the steps of bead explosion detection, namely converting a three-channel image into three single-channel images, respectively selecting the component distribution of the single-channel images r, g and b after conversion, selecting bead explosion within a gray range from the three single channels, solving a union set between two areas, calculating the parts connected with the areas, selecting a circular part, and counting the number of bead explosion.
3. The method for on-line visual inspection of an exploded bead defect of a transparent filter stick according to claim 2, further comprising the steps of dividing the intensity of each color into 256 levels based on RGB image, from 0 to 255, wherein 255 indicates the most saturated state, 0 indicates the light intensity of 0, which means no light, and converting the image from RGB color space to hsv color space; then, the detection range is narrowed according to the saturation and the chroma information; and performing color segmentation on the image according to the value range of a certain color in the H channel to obtain the color defect of the exploded beads of the transparent filter stick.
4. The method for on-line visual inspection of the bead explosion defect of the transparent filter stick according to claim 1, wherein a digital image is used for carrying out on-line visual inspection on the digital imageDiscrete approximation is made to obtain clearer image details and more proportionally balanced gray levels.
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2019
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