CN114359155A - Film laminating method and system - Google Patents

Film laminating method and system Download PDF

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CN114359155A
CN114359155A CN202111486149.1A CN202111486149A CN114359155A CN 114359155 A CN114359155 A CN 114359155A CN 202111486149 A CN202111486149 A CN 202111486149A CN 114359155 A CN114359155 A CN 114359155A
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image
detection
detected
film
carrying
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郭海光
程俊
高向阳
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Shenzhen Institute of Advanced Technology of CAS
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Shenzhen Institute of Advanced Technology of CAS
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/02—Measures preceding sorting, e.g. arranging articles in a stream orientating
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/36—Sorting apparatus characterised by the means used for distribution
    • B07C5/361—Processing or control devices therefor, e.g. escort memory
    • B07C5/362—Separating or distributor mechanisms
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C63/00—Lining or sheathing, i.e. applying preformed layers or sheathings of plastics; Apparatus therefor
    • B29C63/0004—Component parts, details or accessories; Auxiliary operations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C63/00—Lining or sheathing, i.e. applying preformed layers or sheathings of plastics; Apparatus therefor
    • B29C63/02—Lining or sheathing, i.e. applying preformed layers or sheathings of plastics; Apparatus therefor using sheet or web-like material
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration by the use of local operators
    • G06T5/70—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06T7/0006—Industrial image inspection using a design-rule based approach
    • G—PHYSICS
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • 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
    • G06T7/174—Segmentation; Edge detection involving the use of two or more images
    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
    • B07C2501/0063—Using robots
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20224—Image subtraction
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection

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  • Computer Vision & Pattern Recognition (AREA)
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  • Manufacturing & Machinery (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)

Abstract

The application relates to the technical field of glass preparation, in particular to a film coating method and system. The current glass film coating system generally completes the detection links before and after film coating by independent equipment, and the middle of the system is connected by a conveyor belt. Such a system solution places high demands on the cleanliness of the factory environment. The application provides a film covering method which comprises the steps of carrying out first-time defect detection on materials to be detected, covering films on the materials qualified for detection, carrying out second-time defect detection on the materials after film covering to obtain a second detection result, and classifying the film covering materials according to the second detection result. On the premise of ensuring the precision and the efficiency, the cleanliness requirement on the production environment is greatly reduced, and the investment of a factory is reduced.

Description

Film laminating method and system
Technical Field
The application relates to the technical field of glass preparation, in particular to a film coating method and system.
Background
Glass products are increasingly used in today's life, particularly in the field of electronics. In the production process of the glass products with the requirement on cleanliness, the glass products are generally required to be subjected to film coating and then delivered. In the traditional process, the detection, the film covering and the post-film covering of the glass can be finished by a plurality of devices, and the middle of the devices are connected by a production line conveyor belt. In order to ensure that the products are not contaminated during the transfer, very high requirements are placed on the cleanliness of the plant.
With the rapid development of technology and industrial level, glass products are increasingly applied to electronic products, such as the most common mobile phone screen. Meanwhile, the quality requirements of glass products are higher and higher, and detection of light transmittance, uniformity, impurities, bubbles, water ripples and the like is required. Before leaving the factory, the glass product needs to be pasted with a protective film on the surface to prevent the surface of the glass from being polluted due to scratches or dust particles, and the protective film is pasted on the glass and can not generate air bubbles between the pasted film and the glass. In the traditional processing technology, the quality detection, film coating and post-film coating detection of glass are finished by a plurality of devices, and the devices are connected by a conveyor belt. A procedure of cleaning dirt is required to be added before film covering, and the high requirement is provided for the cleanliness of a factory.
Disclosure of Invention
1. Technical problem to be solved
Based on the current glass film covering system, the detection links before and after film covering are generally completed by independent equipment, and the detection links are connected by a conveyor belt in the middle. Such a system solution places high demands on the cleanliness of the factory environment. The application provides a film laminating method and system.
2. Technical scheme
In order to achieve the purpose, the application provides a film coating method which comprises the steps of carrying out first defect detection on materials to be detected, coating films on materials which are qualified for detection, carrying out second defect detection on the materials after film coating to obtain a second detection result, and classifying the film coating materials according to the second detection result.
Another embodiment provided by the present application is: the method comprises the steps of placing the material to be detected, conveying the material to be detected to a detection laminating mechanism, carrying out visual alignment on the material to be detected, and carrying out first defect detection on the material to be detected.
Another embodiment provided by the present application is: the first defect detection comprises detecting the uniformity, transmittance, cracks, scratches, dust, glass slag, bubbles, hand fingerprints, water marks, tooth edges and openings of the material to be detected.
Another embodiment provided by the present application is: the uniformity and transmittance detection comprises the steps of collecting a light source sample, collecting an image of a material to be detected, converting the light source sample into a first gray scale image, converting the image of the material to be detected into a second gray scale image, solving a data difference image of the first gray scale image and the second gray scale image, carrying out threshold segmentation according to a preset value of transmittance of the material to be detected and the data difference image to obtain a binary image, solving connected domains of the binary image to obtain a label matrix, a state matrix and a centroid matrix of different connected domains, and judging uniformity and transmittance after solving a pixel mean value in a corresponding connected domain region in the data difference image according to the state matrix.
Another embodiment provided by the present application is: the method comprises the steps of setting an interested area, obtaining a detection image of a material to be detected, converting the detection image into a third gray-scale image, carrying out Gaussian filtering on the third gray-scale image, removing interference in the image by a smooth image to obtain a second image, carrying out threshold segmentation on the second image by adopting a local threshold segmentation algorithm to obtain a third image, carrying out edge detection on the third image to extract defect contour information, counting the defect information according to the defect contour information, comparing the defect information with a detection standard to obtain all suspicious defects of the material to be detected, and judging whether the material to be detected is qualified.
Another embodiment provided by the present application is: the tooth edge and opening detection comprises the steps of setting a mask, extracting an interested region, and multiplying the interested region mask and the image of the material to be detected to obtain an interested region image; and performing Gaussian filtering on the image of the region of interest to obtain a fourth image, converting the fourth image into a fourth gray-scale map, binarizing the fourth gray-scale map information to obtain a second binarized image, solving a second connected domain in the second binarized image, calculating the size of the second connected domain, and comparing the calculation result with a detection standard value to judge whether the material to be detected is qualified.
Another embodiment provided by the present application is: the second defect detection includes detecting bubbles and dust of the coating.
The application also provides a film laminating system corresponding to the film laminating method, which comprises a feeding assembly line mechanism, a feeding mechanism, a detection film laminating mechanism, a discharging mechanism and a control analysis mechanism; the feeding assembly line mechanism, the film detection and coating mechanism and the blanking mechanism are arranged in sequence; the feeding assembly line mechanism is connected with the control analysis mechanism, the feeding mechanism is connected with the control analysis mechanism, the detection film coating mechanism is connected with the control analysis mechanism, the discharging mechanism is connected with the control analysis mechanism, and the detection film coating mechanism is used for detecting materials before film coating, after film coating and film coating, and transmitting data to the control analysis mechanism.
Another embodiment provided by the present application is: the film detection and covering mechanism is multiple, and the feeding mechanism is a mechanical arm.
Another embodiment provided by the present application is: the detection film covering mechanism comprises a first image collector, a second image collector and a film covering machine, the first image collector is connected with the control analysis mechanism, the second image collector is connected with the control analysis mechanism, and the film covering machine is connected with the control analysis mechanism.
3. Advantageous effects
Compared with the prior art, the film laminating method and the film laminating system have the advantages that:
the laminating method provided by the application greatly reduces the requirement on the cleanliness of the production environment on the premise of ensuring the precision and the efficiency, and reduces the investment of a factory.
The film laminating method provided by the application adopts different detection algorithms aiming at different defects, so that the false detection rate is greatly reduced.
The film coating method provided by the application is a method integrating detection, film coating and post-film coating detection, reduces the probability of pollution, and reduces the requirement on the factory environment; meanwhile, in order to improve the efficiency, a multi-station parallel solution is designed.
According to the film laminating method, the film laminating mechanisms are detected to be multiple, and the multiple stations are arranged in parallel, so that the production efficiency is improved.
The application provides a tectorial membrane system, the detection process integration around with the tectorial membrane is in an equipment, simplifies the assembly line, reduces the cleanliness factor requirement to the mill.
The film coating method can simplify the production line of glass product film coating, reduce the requirement of factory cleanliness, and improve the detection efficiency through parallel processing.
Drawings
FIG. 1 is a schematic structural view of a film coating system of the present application;
FIG. 2 is a schematic diagram of the control analysis software main interface of the present application;
FIG. 3 is a schematic view of the detection mechanism of the present application;
fig. 4 is a schematic diagram of the terminal device of the present application.
Detailed Description
Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings, and it will be apparent to those skilled in the art from this detailed description that the present application can be practiced. Features from different embodiments may be combined to yield new embodiments, or certain features may be substituted for certain embodiments to yield yet further preferred embodiments, without departing from the principles of the present application.
Referring to fig. 1 to 4, the application provides a film coating method, which includes performing first defect detection on a material to be detected, coating a film on the material which is qualified for detection, performing second defect detection on the material after film coating to obtain a second detection result, and classifying the material after film coating according to the second detection result.
Further, the first defect detection of the material to be detected comprises the steps of placing the material to be detected, conveying the material to be detected to a detection film covering mechanism, and performing first defect detection on the material to be detected after visual alignment of the material to be detected.
In the application, three functions of pre-film-coating detection, film coating and post-film-coating detection of the glass product are integrated. The full automation of processing and sorting is realized. The system flow mainly comprises the following six links:
(1) the placing positions of the products are random when the products are conveyed from the assembly line, and (1) the products can be unified into a fixed placing angle after passing through a clamping mechanism at the tail end of the feeding assembly line mechanism 1. Then a mechanical sucker of the feeding mechanism 2 can suck materials from the tail end of the production line to the detection film covering mechanism; (2) because the mechanical sucker of the feeding mechanism 2 places the material into the fixture of the detection film covering mechanism, a certain position deviation exists, the two sides of the length and the width of the detection film covering mechanism are respectively provided with an infrared laser beam which is parallel to the detection film covering mechanism, and the detection film covering mechanism enables the linear laser beam not to be shielded by the edge of the material through fine adjustment, so that the accurate positioning of the material is realized; (3) moving the film covering detection mechanism to a detection station 8, and starting a plurality of defect detections by the system; (4) determining whether film covering is needed or not according to the detection result, and finishing the film covering; (5) detecting defects after film coating; (6) the blanking mechanism 3 blanks to the production line, and the control analysis mechanism 4 distinguishes according to the detection result.
Further, the first defect detection comprises detecting uniformity, transmittance, cracks, scratches, dust, glass residue, bubbles, fingerprints, water marks, tooth edges and openings of the material to be detected.
Further, the uniformity and transmittance detection comprises the steps of collecting a light source sample, collecting an image of a material to be detected, converting the light source sample into a first gray scale image, converting the image of the material to be detected into a second gray scale image, obtaining a data difference image of the first gray scale image and the second gray scale image, carrying out threshold segmentation according to a preset value of transmittance of the material to be detected and the data difference image to obtain a binary image, obtaining connected domains of the binary image to obtain a label matrix, a state matrix and a centroid matrix of different connected domains, and judging uniformity and transmittance after obtaining a pixel mean value in a corresponding connected domain region in the data difference image according to the state matrix.
Further, the crack, scratch, dust, glass residue, bubble, fingerprint and water mark detection comprises the steps of setting an interested area, obtaining a detection image of the material to be detected, converting the detection image into a third gray-scale image, carrying out Gaussian filtering on the third gray-scale image, smoothing the image, removing interference in the image to obtain a second image, carrying out threshold segmentation on the second image by adopting a local threshold segmentation algorithm to obtain a third image, carrying out edge detection on the third image to extract defect contour information, carrying out statistics on the defect information according to the defect contour information, comparing the defect information with a detection standard to obtain all suspicious defects of the material to be detected, and judging whether the material to be detected is qualified.
Further, the tooth edge and opening detection comprises the steps of setting a mask, extracting an interested region, and multiplying the interested region mask and the image of the material to be detected to obtain an interested region image; and performing Gaussian filtering on the image of the region of interest to obtain a fourth image, converting the fourth image into a fourth gray-scale map, binarizing the fourth gray-scale map information to obtain a second binarized image, solving a second connected domain in the second binarized image, calculating the size of the second connected domain, and comparing the calculation result with a detection standard value to judge whether the material to be detected is qualified.
Further, the second defect detection includes detection of bubbles and dust of the coating film.
The application also provides a film laminating system corresponding to the film laminating method, which comprises a feeding assembly line mechanism 1, a feeding mechanism 2, a detection film laminating mechanism, a discharging mechanism 3 and a control analysis mechanism 4; the feeding assembly line mechanism 1, the detection film coating mechanism and the blanking mechanism 3 are arranged in sequence; the feeding assembly line mechanism 1 is connected with the control analysis mechanism 4, the feeding mechanism 2 is connected with the control analysis mechanism 4, the detection film covering mechanism is connected with the control analysis mechanism 4, the discharging mechanism 3 is connected with the control analysis mechanism 4, the detection film covering mechanism is used for detecting materials before film covering, covering films and detecting after the film covering, and transmitting data to the control analysis mechanism 4.
The feeding assembly line mechanism 1, the feeding mechanism 2, the detection film covering mechanism and the discharging mechanism 3 are respectively in data interaction with the control analysis mechanism 4, and the control analysis mechanism 4 controls the feeding assembly line mechanism 1, the feeding mechanism 2, the detection film covering mechanism and the discharging mechanism 3 and processes collected data.
Further, it is a plurality of to detect the tectorial membrane mechanism, feed mechanism 2 is the manipulator.
Further, the film detection and coating mechanism comprises a first image collector, a second image collector and a film coating machine 5, the first image collector is connected with the control analysis mechanism 4, the second image collector is connected with the control analysis mechanism 4, and the film coating machine 5 is connected with the control analysis mechanism 4. The film covering detection mechanism comprises a detection station 8, and the detection station 8 is matched with the first image collector, the second image collector and the film covering machine 5 for use.
Examples
(1) Incoming material defect detection
Because the suppliers of the glass products have different delivery detection specifications for the products and can pollute the supplied glass products in the transportation and unpacking process, the glass products need to be detected in detail before film coating, and bad materials are removed. The following defects are mainly detected in detail in the present application: uniformity, transparency, cracks, scratches, dust, glass residue, bubbles, fingerprints, water marks, edges, and holes. These defects are classified into three main categories according to their performance characteristics:
1) uniformity and transmittance
2) Cracks, scratches, fingerprints, water marks, dust, glass residues, bubbles
3) Tooth edge, open pore
In order to better detect the defects, the image acquisition is designed specifically in the application. As shown in fig. 3: two high-resolution cameras are arranged, the first image collector comprises a first camera 6 and a light source, the first camera 6 is located right above the detection station 8, the second image collector comprises a second camera 7 and a light source, and the second camera 7 is located above the side of the detection station 8. Two strip-shaped light sources are arranged at the side of the detection station 8; a surface light source is arranged directly below the inspection station 8. When detecting different types of defects, adopting different detection methods:
1) uniformity and transmittance
First, a light source sample is collected. And (3) no object to be detected is placed at the detection station 8, the two lateral strip-shaped light sources are closed, the surface light source below is opened, and the surface light source sample A is collected by using the right-above image collector.
Then, the glass image to be inspected is acquired. And the detection station 8 is used for placing the glass panel to be detected, closing the strip-shaped light sources at two sides, opening the surface light source at the lower part, and collecting the image B to be detected by using the image collector right above.
Finally, the software analyzes to obtain the result. The analysis was as follows:
firstly, respectively converting a surface light source sample A and an image B to be detected into an 8bit gray level image to obtain AgrayAnd Bgray。
Secondly, two image data difference images C are obtaineddiffThe absdiff function of opencv can be used directly:
absdiff(Agray，Bgray，Cdiff)
thirdly, the difference image C is aligned according to the preset values a and b of the transmittance of the productdiffPerforming threshold segmentation:
threshold(Cdiff，Dbin，a，b，THRESH-BINARY)
obtaining a binarized image Dbin。
Fourthly, then the image D is alignedbinAnd (3) carrying out connected domain calculation:
connectedComponentsWithStats(Dbin，Olabel，Ostat，Ocentroid)
respectively obtaining label matrixes O of different connected domainslabelConnected domain state matrix OstatAnd the centroid matrix O of the connected domaincertroid。
Fifthly, according to the connected domain state matrix OstatFinding difference image C in sequencediffIf the mean value is larger than the threshold value set by the software, the light transmittance of the pixel mean value in the corresponding connected domain area is judged to be unqualified; and after the mean value of the pixels in the corresponding connected domain in all the difference images is obtained, calculating the variance of the difference images, and if the variance value is greater than a threshold value set by software, judging that the light transmission uniformity of the material is unqualified.
2) Cracks, scratches, fingerprints, water marks, dust, glass residues, bubbles
Firstly, setting an interested ROI (region of interest) of a detection station 8 through software, wherein the information is used for eliminating an irrelevant region when an image is acquired;
then, a detection image is acquired. Opening a surface light source below the detection station 8, and respectively capturing an image by the first camera 6 and the second camera 7; the area light source is turned off, the strip light sources on the two sides are turned on, and the first camera 6 and the second camera 7 respectively capture one image. Four images to be detected are obtained.
Next, the acquired images are analyzed one by one. The analysis flow is as follows:
firstly, converting an image into an 8-bit gray-scale image to obtain Pgray
cvtColor(Proi，Pgray，COLOR_RGB2GRAY)
Wherein, ProiThe image is the collected image after ROI processing.
Performing Gaussian filtering on the image, and smoothing the image to remove interference in the image;
GaussianBlur(Pgray，Pgaus，Size(3，3)，0)
wherein, PgausFor the output image after gaussian filtering, Size (3, 3) is the Size of the gaussian kernel, and finally the parameter 0 represents the standard deviation in the x-direction.
And thirdly, a qualified standard is preset during production detection of actual products, and in order to better extract all defects of the products exceeding the set value from the images and filter small defects in a standard range, the images need to be subjected to threshold segmentation and then processed. In consideration of the non-uniformity of the actual illumination environment, the global threshold segmentation algorithm cannot be compatible with the situation of each part of the image, and the segmentation effect is affected, so that a local threshold segmentation algorithm needs to be used here.
adaptiveThreshold(Pgaus，Pth，255，ADAPTIVE_THRESH_MEAN_C，THRESH_BINARY，n，c)
Wherein, PthIs an output image after being segmented by using a local threshold; the parameter three '255' is the maximum value representing the pixel point, and the 8-bit gray scale image is adopted here, so the maximum value is 255; ADAPTIVE _ THRESH _ MEAN _ C represents a way of calculating the average value of pixels in a local neighborhood and then eliminating a constant C; THRESH _ BINARY represents a threshold segmentation type; the parameter six "n" represents the neighborhood size used in calculating the local threshold; the parameter seven "C" represents a constant C that needs to be corrected when calculating the threshold value, and needs to be set in software according to the actual lighting situation.
And fourthly, the image of the normal product in the illumination environment is continuous and smooth, and when obvious defects exist, obvious discontinuity phenomena appear near the defects. The contours of these defects can be extracted by performing edge detection on the image after threshold segmentation.
Canny(pth，Pedge，min，max)
The parameters min and max represent minimum and maximum thresholds for edge detection.
After the defect contour information exists, each single defect information can be counted out through an algorithm:
connectedComponentsWithStats(Pedge，Qlabel，Qstat，Qcentroid)
wherein, OlabelA matrix of all defective labels; o isstatA state matrix for all defect images; o iscertroidCentroid matrix for all defects
And finally, after the detection of 4 collected images of a material is finished, comparing the defect information in the images with the test standard one by one, counting all suspicious defects of the material, and then judging whether the material is qualified.
3) Tooth edge, open pore
The tooth edges and the opening edges are generally subjected to circular arc chamfering treatment, some characteristic differences exist between the tooth edges and the opening edges and need to be extracted and treated independently during detection. The processing flow is as follows:
firstly, setting a mask through software, extracting an interested region, multiplying the interested region mask and an image to be processed to obtain an interested region image, wherein the image value in the interested region is kept unchanged, and the image value outside the interested region is 0. This function can be done using the following commands in opencv:
bitwise_and(Pin，Pin，Pout，mask)
wherein, PinFor the complete picture collected by the first camera and the second camera, mask is a mask set by software, PoutTo mask the image behind extraneous regions.
Then, the following steps are carried out for detection:
to image PoutGaussian filtering is carried out to remove some interference in the image to obtain Pblur。
② image PblurGray scale P converted to 8bitgray。
Setting proper threshold value, binary image information to obtain PbinTo make the tooth edge and opening edge of the glass in the image more clear
Fourthly, obtaining the image PbinAnd calculating the size of each connected domain. And then comparing with a preset detection standard value of normal materials.
a, whether the maximum connected domain meets the range or not, if the maximum connected domain exceeds the range, tooth edges have problems.
b, judging whether a connected domain with a small value exists, if so, marking the defect by using a minimum bounding rectangle.
c, solving the area of the connected domain of the opening type target, if the area exceeds a set range, the opening is abnormal, and marking the opening by using a minimum external rectangle.
If any abnormality exists in the above steps, the material is marked to be abnormal, otherwise, the material is marked to be qualified.
And after the detection is finished, controlling the mechanism to perform corresponding operation according to the detection result. If the incoming material has no defect exceeding the standard, the control and analysis mechanism 4 is informed to enter the film covering process; if the supplied materials are unqualified, the detection film coating mechanism directly returns to the feeding position from the detection station 8, and the discharging mechanism 3 directly takes out the supplied materials from the detection film coating mechanism and puts the supplied materials to a conveyor belt, so that the supplied materials flow to an unqualified area. Then, the feeding mechanism 2 grabs the next material to be detected to the detection film covering mechanism.
(2) Film coating
And (5) detecting qualified incoming materials and starting a film covering process.
Firstly, the strip-shaped light sources at two sides descend to a parking position and are turned off;
then, the laminator 7 located above the inspection station 8 descends, and a series of laminating operations are completed by the operation of the mechanism.
The laminating machine comprises a film rolling component, a roller, a positioning sensor, a vacuumizing component and a film cutting component. Wherein: the film rolling assembly is used for conveying the adhesive tape for film sticking; the roller is used for peeling the sticking film from the film rolling mechanism and rolling the sticking film on the glass product; the positioning sensor is used for determining the starting position and the ending position of the film covering; the vacuumizing assembly is used for being matched with the roller device to evacuate air between the film and the glass material so as to prevent bubbles; the film cutting assembly cuts off the edge of the film according to the positioning sensor, and the redundant film is recovered by the other end of the film rolling mechanism.
(3) Post-lamination detection
After the film covering is finished, the product needs to be detected, and whether the film covering is qualified or not is judged.
The items detected mainly include bubble and dust detection.
First, the institution prepares to enter a detection procedure. The laminator 7 is raised and the strip light sources at both sides of the detection station 8 are raised.
Then, an image for detection is acquired. Opening a surface light source below the detection station 8, and respectively capturing an image by the first camera 7 and the second camera 7; the area light source is turned off, the strip light sources on the two sides are turned on, and the first camera 6 and the second camera 7 respectively capture one image. Four images to be detected are obtained.
And then, sequentially checking the images, wherein the specific method is the same as the incoming material defect detection method II.
(4) Discharging
After detection is finished, the film covering mechanism returns to the material loading position, and the blanking mechanism 3 blanks products on different conveyor belts according to detection results.
The present application further provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps in any of the method embodiments described above are implemented.
The terminal device of this embodiment includes: at least one processor (only one shown in fig. 4) a processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor when executing the computer program implementing the steps in any of the various metabolic pathway prediction method embodiments described below.
The terminal device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The terminal device may include, but is not limited to, a processor, a memory. Those skilled in the art will appreciate that the terminal device is merely an example, and does not constitute a limitation of the terminal device, and may include more or less components than those shown, or combine some components, or different components, such as input and output devices, network access devices, etc.
The Processor may be a Central Processing Unit (CPU), or other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may in some embodiments be an internal storage unit of the terminal device, such as a hard disk or a memory of the terminal device. In other embodiments, the memory may also be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (MC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal device.
Further, the memory may also include both an internal storage unit and an external storage device of the terminal device. The memory is used for storing an operating system, application programs, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer programs. The memory may also be used to temporarily store data that has been output or is to be output.
The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned method embodiments.
The embodiments of the present application provide a computer program product, which when running on a terminal device, enables the terminal device to implement the steps in the above method embodiments when executed. The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application. In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Although the present application has been described above with reference to specific embodiments, those skilled in the art will recognize that many changes may be made in the configuration and details of the present application within the principles and scope of the present application. The scope of protection of the application is determined by the appended claims, and all changes that come within the meaning and range of equivalency of the technical features are intended to be embraced therein.

Claims (10)

1. A film laminating method is characterized in that: the method comprises the steps of carrying out first defect detection on materials to be detected, carrying out film covering on materials qualified for detection, carrying out second defect detection on the materials subjected to film covering to obtain a second detection result, and classifying the film covering materials according to the second detection result.
2. The method of claim 1, wherein: the method comprises the steps of placing the material to be detected, conveying the material to be detected to a detection laminating mechanism, carrying out visual alignment on the material to be detected, and carrying out first defect detection on the material to be detected.
3. The method of claim 1, wherein: the first defect detection comprises detecting the uniformity, transmittance, cracks, scratches, dust, glass slag, bubbles, hand fingerprints, water marks, tooth edges and openings of the material to be detected.
4. A method of coating as claimed in claim 3, wherein: the uniformity and transmittance detection comprises the steps of collecting a light source sample, collecting an image of a material to be detected, converting the light source sample into a first gray scale image, converting the image of the material to be detected into a second gray scale image, solving a data difference image of the first gray scale image and the second gray scale image, carrying out threshold segmentation according to a preset value of transmittance of the material to be detected and the data difference image to obtain a binary image, solving connected domains of the binary image to obtain a label matrix, a state matrix and a centroid matrix of different connected domains, and judging uniformity and transmittance after solving a pixel mean value in a corresponding connected domain region in the data difference image according to the state matrix.
5. A method of coating as claimed in claim 3, wherein: the method comprises the steps of setting an interested area, obtaining a detection image of a material to be detected, converting the detection image into a third gray-scale image, carrying out Gaussian filtering on the third gray-scale image, removing interference in the image by a smooth image to obtain a second image, carrying out threshold segmentation on the second image by adopting a local threshold segmentation algorithm to obtain a third image, carrying out edge detection on the third image to extract defect contour information, counting the defect information according to the defect contour information, comparing the defect information with a detection standard to obtain all suspicious defects of the material to be detected, and judging whether the material to be detected is qualified.
6. A method of coating as claimed in claim 3, wherein: the tooth edge and opening detection comprises the steps of setting a mask, extracting an interested region, and multiplying the interested region mask and the image of the material to be detected to obtain an interested region image; and performing Gaussian filtering on the image of the region of interest to obtain a fourth image, converting the fourth image into a fourth gray-scale map, binarizing the fourth gray-scale map information to obtain a second binarized image, solving a second connected domain in the second binarized image, calculating the size of the second connected domain, and comparing the calculation result with a detection standard value to judge whether the material to be detected is qualified.
7. The method of claim 1, wherein: the second defect detection includes detecting bubbles and dust of the coating.
8. A film coating system corresponding to the film coating method according to any one of claims 1 to 7, characterized in that: the device comprises a feeding assembly line mechanism, a feeding mechanism, a detection film coating mechanism, a discharging mechanism and a control analysis mechanism; the feeding assembly line mechanism, the film detection and coating mechanism and the blanking mechanism are arranged in sequence; the feeding assembly line mechanism is connected with the control analysis mechanism, the feeding mechanism is connected with the control analysis mechanism, the detection film coating mechanism is connected with the control analysis mechanism, the discharging mechanism is connected with the control analysis mechanism, and the detection film coating mechanism is used for detecting materials before film coating, after film coating and film coating, and transmitting data to the control analysis mechanism.
9. The laminating system of claim 8, wherein: the film detection and covering mechanism is multiple, and the feeding mechanism is a mechanical arm.
10. The laminating system of claim 8, wherein: the detection film covering mechanism comprises a first image collector, a second image collector and a film covering machine, the first image collector is connected with the control analysis mechanism, the second image collector is connected with the control analysis mechanism, and the film covering machine is connected with the control analysis mechanism.
CN202111486149.1A 2021-12-07 2021-12-07 Film laminating method and system Pending CN114359155A (en)

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Application Number Priority Date Filing Date Title
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114627110A (en) * 2022-05-12 2022-06-14 济宁海富光学科技有限公司 Detection method for light transmission abnormity of glass cover plate
CN115578462A (en) * 2022-11-18 2023-01-06 深圳市全正科技有限公司 Machine vision positioning control system applied to automatic opposite pasting of display screen optical films

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114627110A (en) * 2022-05-12 2022-06-14 济宁海富光学科技有限公司 Detection method for light transmission abnormity of glass cover plate
CN114627110B (en) * 2022-05-12 2022-08-16 济宁海富光学科技有限公司 Detection method for light transmission abnormity of glass cover plate
CN115578462A (en) * 2022-11-18 2023-01-06 深圳市全正科技有限公司 Machine vision positioning control system applied to automatic opposite pasting of display screen optical films
CN115578462B (en) * 2022-11-18 2023-03-07 深圳市全正科技有限公司 Machine vision positioning control system applied to automatic opposite pasting of display screen optical films

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