WO2021124775A1 - Procédé de classification de défaut, dispositif de classification de défaut et procédé de production d'article en verre - Google Patents

Procédé de classification de défaut, dispositif de classification de défaut et procédé de production d'article en verre Download PDF

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Publication number
WO2021124775A1
WO2021124775A1 PCT/JP2020/043010 JP2020043010W WO2021124775A1 WO 2021124775 A1 WO2021124775 A1 WO 2021124775A1 JP 2020043010 W JP2020043010 W JP 2020043010W WO 2021124775 A1 WO2021124775 A1 WO 2021124775A1
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Prior art keywords
defect
classification
image data
classifier
defects
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PCT/JP2020/043010
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English (en)
Japanese (ja)
Inventor
貴裕 茗原
幹将 北川
厚司 井上
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日本電気硝子株式会社
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Priority to CN202080071666.XA priority Critical patent/CN114556414A/zh
Priority to KR1020227010768A priority patent/KR20220113348A/ko
Publication of WO2021124775A1 publication Critical patent/WO2021124775A1/fr

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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
    • G01N21/8851—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
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/22—Measuring arrangements characterised by the use of optical techniques for measuring depth
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • 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
    • 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
    • G—PHYSICS
    • G02—OPTICS
    • G02F—OPTICAL DEVICES OR ARRANGEMENTS FOR THE CONTROL OF LIGHT BY MODIFICATION OF THE OPTICAL PROPERTIES OF THE MEDIA OF THE ELEMENTS INVOLVED THEREIN; NON-LINEAR OPTICS; FREQUENCY-CHANGING OF LIGHT; OPTICAL LOGIC ELEMENTS; OPTICAL ANALOGUE/DIGITAL CONVERTERS
    • G02F1/00—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics
    • G02F1/01—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour 
    • G02F1/13—Devices or arrangements for the control of the intensity, colour, phase, polarisation or direction of light arriving from an independent light source, e.g. switching, gating or modulating; Non-linear optics for the control of the intensity, phase, polarisation or colour  based on liquid crystals, e.g. single liquid crystal display cells
    • G02F1/133—Constructional arrangements; Operation of liquid crystal cells; Circuit arrangements
    • G02F1/1333—Constructional arrangements; Manufacturing methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • 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
    • 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
    • G01N2021/8854—Grading and classifying of flaws
    • 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
    • G01N2021/8887—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 based on image processing techniques
    • 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
    • G06T2207/20081—Training; Learning
    • 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
    • G06T2207/30121—CRT, LCD or plasma display

Definitions

  • the present invention relates to a method and an apparatus for classifying defects contained in a glass article, and a method for manufacturing a glass article by using the method and the apparatus.
  • a glass plate is used for displays such as liquid crystal displays and organic EL displays.
  • glass plates are molded using various manufacturing equipment, but in each case, the glass raw material is heated and melted, the molten glass is homogenized, and then molded into a predetermined shape. It is commonly done. In this manufacturing method, defects may occur on the surface or inside of the glass plate due to various causes. Therefore, the technique of accurately measuring the defect of the glass plate is very important.
  • Patent Document 1 discloses a method for manufacturing a glass plate including an inspection step of measuring the dimensions and depth (distance from the glass surface) of defects existing inside the glass plate with a laser microscope.
  • an inspector operates a laser microscope to measure defects.
  • the present invention has been made in view of the above circumstances, and it is a technical subject to improve the work efficiency of inspection by improving the accuracy of classification of the types of defects related to glass articles with a classifier.
  • the present invention is for solving the above-mentioned problems, and is a method of detecting a defect contained in a glass article as image data and classifying the type of the defect by a defect classification device based on the image data.
  • the types of defects include a first classification and a second classification
  • the defect classification device includes a first classifier created by machine learning the first teacher data related to the first classification, and the first classifier.
  • a second classifier created by machine learning the second teacher data related to the second classification is provided, and whether or not the defect belongs to the first classification is determined by the first classifier based on the image data.
  • the second determination step is provided.
  • the first classifier that performs the first determination step and the second classifier that performs the second determination step are created by machine learning the first teacher data and the second teacher data. , It is possible to accurately classify the types of defects related to the first classification and the second classification.
  • the correct answer rate of the first classifier may be higher than the correct answer rate of the second classifier.
  • the first teacher data according to the first classification includes image data related to defects formed at a predetermined depth of the glass article, and before the first determination step.
  • a depth determination step for determining whether or not the depth of the defect in the image data detected from the glass article is the predetermined depth is provided, and the first determination step is the depth determination step. , Can be executed when the depth of the defect is determined to be the predetermined depth.
  • the image data related to the first teacher data may include only image data related to bubbles, and the image data related to the second teacher data contains only image data related to platinum foreign matter. It may be included.
  • the image data regarding the bubble may include information regarding the shape and / or color of the bubble.
  • information regarding the shape and / or color of the bubble in the first teacher data, it is possible to subdivide the type of the defect related to the bubble into the shape and the color.
  • the image data regarding the platinum foreign substance may include information regarding the shape and / or color of the platinum foreign substance. In this way, by including the information in the shape and / or color of the platinum foreign substance in the second teacher data, it is possible to subdivide the type of the defect related to the platinum foreign substance into the shape and color.
  • the present invention is for solving the above-mentioned problems, and is a method for manufacturing a glass article, which is based on an imaging step of acquiring image data of defects contained in the glass article and any of the above based on the image data. It is characterized by comprising a step of classifying the type of the defect by the defect classification method.
  • the present invention is for solving the above-mentioned problems, and is an apparatus for detecting a defect contained in a glass article as image data and classifying the type of the defect based on the image data.
  • a first classifier created by machine learning the first teacher data related to the first classification, including the first classification and the second classification, and the defect is found based on the image data. It is a first classifier that determines whether or not it belongs to the first classification, and a second classifier created by machine learning the second teacher data related to the second classification, and the defect is the first. It is characterized by including a second classifier that determines whether or not the defect belongs to the second category based on the image data when it is determined that the defect does not belong to one category.
  • the defect classification device by automating the classification of the type of defect by the defect classification device, it becomes possible to efficiently perform the work related to the inspection of the defect. Since the first classifier and the second classifier of the defect classification device according to the present invention are created by machine learning the first teacher data and the second teacher data, the classification of the defect type can be performed accurately. It can be carried out.
  • the present invention it is possible to improve the work efficiency of inspection by accurately classifying the types of defects related to glass articles with a classifier.
  • FIG. 1 to 12 show a method for manufacturing a glass article according to the present invention and an embodiment of an inspection device used in the method.
  • a case of manufacturing a transparent glass plate as an example of a glass article will be described.
  • the inspection device 1 includes an image pickup device 3 that captures an image of a glass plate G mounted on a mounting table 2, and an image processing device that performs image processing on image data acquired by the image pickup device 3. 4 and a defect classification device 5 for classifying the types of defects included in the image data.
  • the image pickup device 3 is provided with an optical microscope, and can acquire a magnified image of the surface S and the inside of the glass plate G as data.
  • the image pickup apparatus 3 is configured to be movable in the vertical direction and the horizontal direction by the moving mechanism 3a.
  • the image pickup apparatus 3 can change the position of the focal point with respect to the thickness direction of the glass plate G by moving in the vertical direction at a predetermined pitch. As a result, the image pickup apparatus 3 can take an image of a defect contained inside the glass plate G.
  • the image pickup device 3 is connected to the image processing device 4.
  • the image pickup device 3 transmits the acquired image data to the image processing device 4.
  • the image processing device 4 includes a PC that incorporates various hardware such as a display 4a and an input interface, in addition to incorporating a CPU, GPU, RAM, ROM, HDD, SDD, and the like. As shown in FIG. 2, the image processing device 4 is located between an arithmetic processing unit 6 composed of a CPU or the like, a storage unit 7 composed of an HDD, an SSD, or the like, an image pickup device 3, and a defect classification device 5. It includes a communication unit 8 for transmitting and receiving data.
  • the image processing device 4 controls the operation of the image pickup device 3 by executing the image analysis program stored in the storage unit 7 by the arithmetic processing unit 6.
  • the image processing device 4 can receive a large number of image data acquired by the image pickup device 3 from the image pickup device 3 and execute various image processes on each image data.
  • the defect classification device 5 includes a PC having built-in hardware such as a CPU, GPU, RAM, ROM, HDD, and SSD. As shown in FIG. 2, the defect classification device 5 transmits / receives data between the arithmetic processing unit 9 composed of a CPU or the like, the storage unit 10 composed of an HDD, SSD, or the like, and the image processing device 4. It is provided with a communication unit 11 for performing.
  • the arithmetic processing unit 9 can classify the types of defects included in the image data acquired by the image pickup apparatus 3 by executing the image analysis program stored in the storage unit 10. Further, the arithmetic processing unit 9 can perform machine learning for classifying the types of defects by using the teacher data stored in the storage unit 10 by executing the image analysis program.
  • the storage unit 10 stores an image analysis program for classifying defects.
  • a deep learning tool program
  • the image analysis program can create an inference model (algorithm) for classifying the types of defects by machine learning (deep learning) based on teacher data.
  • the storage unit 10 includes a first classifier 12 that classifies the types of defects contained in the glass plate G into the first classification, and a second classifier 13 that classifies the types of defects into the second classification.
  • Each of the classifiers 12 and 13 is an inference model created by the arithmetic processing unit 9 by machine learning related to the image analysis program.
  • defects related to "foam” are set to the first category
  • defects related to "platinum foreign matter” are set to the second category.
  • the bubbles as defects include bubbles containing some gas or vacuum bubbles containing no gas.
  • the types of gas are oxygen, carbon dioxide, carbon monoxide, nox (NO X ), nitrogen, chlorine, brom, hydrogen, argon, helium, neon, xenone, steam, socks (SO). X ), sulfurous acid gas, etc.
  • the component at the time of bubble generation may be precipitated as a solid on the inner wall of the bubble in some state.
  • Defects related to "platinum foreign matter” are composed of fine platinum that has shielding properties rather than being transparent to visible light like bubbles. Defects due to "platinum foreign matter” are formed inside the glass plate G, for example, by mixing minute platinum generated from a platinum container for supplying and transferring molten glass for forming the glass plate G into the molten glass. To.
  • the first classifier 12 is an inference model created by performing machine learning on the first teacher data including a plurality of image data related to "bubbles".
  • the first teacher data includes only the image data related to "bubbles" and does not include the image data related to "platinum foreign matter”.
  • the image data of the "bubble" in the first teacher data includes information on the shape and / or color of the bubble.
  • Examples of information related to the shape of bubbles include “horizontal”, “vertical”, and “excessive dimensions”.
  • the shape related to “horizontally long” refers to the shape of a flat bubble extending in the lateral direction as shown in FIG.
  • the shape related to “vertical” refers to the shape of a flat bubble extending in the vertical direction.
  • the shape related to “excessive size” means a “horizontal” shape (or “vertical” shape) in which the entire shape of the bubble is not projected in the range of the screen.
  • An example of information about the "color” of a bubble is "white”. Specifically, each image data of the first teacher data is classified into sub-classifications such as “horizontal”, “vertical”, “excessive size”, and “white”, and each image data is associated with the sub-classification. Machine learning at.
  • the second classifier 13 is an inference model created by performing machine learning on the second teacher data including a plurality of image data related to "platinum foreign matter".
  • the second teacher data includes only the image data related to the "platinum foreign substance” and does not include the image data related to the "bubble".
  • the image data of the "platinum foreign body" in the second teacher data includes information on the shape and / or color of the platinum foreign body.
  • Examples of information on the shape of platinum foreign matter include “triangle”, “polygon”, and “circle”.
  • the shape related to the “triangle” refers to the shape of a platinum foreign substance exhibiting a triangular shape.
  • the shape related to the “polygon” refers to the shape of a polygonal platinum foreign substance excluding a triangle.
  • the shape related to the “circle” refers to the shape of a circular platinum foreign substance.
  • the information regarding the "color” of the platinum foreign substance for example, "white” can be mentioned. Specifically, each image data of the second teacher data is classified into sub-classifications such as “triangle", “polygon”, “circle”, and “white”, and each image data is associated with the sub-classification. Machine learning.
  • this method mainly includes a cutting step S1, an end face processing step S2, a cleaning step S3, and an inspection step S4.
  • the glass original plate supplied to the cutting step S1 is formed by continuously forming a single (strip-shaped) glass ribbon from the molten glass by, for example, a forming apparatus capable of executing an overflow down draw method, and a rectangular glass original plate is formed from the glass ribbon. It is obtained by cutting out.
  • the float method may be used for molding the glass ribbon.
  • the cutting step S1 by cutting the original glass plate, one or a plurality of rectangular glass plates G are cut out to a desired size. Cutting is performed, for example, by forming a scribe line on a glass original plate and then folding it.
  • the glass plate G that has undergone the end face processing step S2 is cleaned.
  • the inspection step S4 includes a first inspection step of inspecting the presence or absence of defects in the glass plate G and a second inspection step of inspecting the defects when the glass plate G contains defects.
  • the glass plate G is inspected for defects by a known inspection device (not shown) arranged on the upstream side of the inspection device 1.
  • a known inspection device for example, an edge light type inspection device is used, but the inspection device is not limited to this.
  • the glass plate G in which defects are detected is further inspected in the second inspection step.
  • Other glass plates G are certified as non-defective products.
  • the second inspection step includes an imaging step S41 for capturing an image of a defect contained in the glass plate G and acquiring the image data, and a measuring step S42 for measuring the state of the defect contained in the image data.
  • a classification step S43 for classifying the types of defects and a quality determination step S44 for determining the quality of the glass plate G (defect) are provided.
  • the image processing device 3 takes a picture of the glass plate G by the operator (inspector) operating the image processing device 4 or by automatic control.
  • the imaging device 3 is moved by the operation of the moving mechanism 3a so that the defect F contained in the glass plate G is located in the field of view.
  • the image pickup apparatus 3 images the surface S and the inside of the glass plate G a plurality of times while moving in the vertical direction at a predetermined pitch.
  • the image data of the glass plate G acquired by the image pickup device 3 is transmitted to the image processing device 4.
  • the arithmetic processing unit 6 of the image processing device 4 stores the received image data in the storage unit 7. Subsequently, the defect F is measured by the image analysis program of the image processing device 4.
  • the size and depth of the defect F are measured.
  • the size (maximum dimension) of the defect F is measured by, for example, an image analysis program.
  • the depth D of the defect F means the distance from the surface S to the defect F in the thickness direction of the glass plate G.
  • the depth D of the defect F is measured, for example, as follows.
  • the most focused image data is selected by the image analysis program of the image processing device 4. Then, the distance from the focal position (coordinates) of the image pickup apparatus 3 to the surface S of the glass plate G in the selected image data is calculated as the depth D of the defect F. Information on the measured size and depth D of the defect F is stored in the storage unit 7 together with the image data.
  • the classification step S43 includes a first classification step of classifying the type of the defect F by the defect classification device 5, and a second classification step of the operator classifying the type of the defect F using the image processing device 4. ..
  • the first classification step includes the first determination step S431 for determining whether or not the defect F belongs to the first classification by the first classifier 12, and whether the defect F belongs to the second classification.
  • a second determination step S432 for determining whether or not to use is provided.
  • the arithmetic processing unit 9 activates the first classifier 12 and determines whether or not the type of the defect F included in the image data belongs to the first classification (“bubble”). In the present embodiment, the arithmetic processing unit 9 determines in the first classifier 12 whether or not the defect F belongs to any of the minor classifications such as "horizontal”, “vertical”, “excessive size", and "white”. .. When the arithmetic processing unit 9 determines that the defect F included in the image data corresponds to the “bubble”, the arithmetic processing unit 9 adds the classification information related to the first classification to the image data and stores it in the storage unit 10. When the first classifier 12 determines that the defect F in the image data is not a "bubble”, the arithmetic processing unit 9 classifies the defect F into "others”, adds the classification information to the image data, and stores the defect F. Store at 10.
  • the arithmetic processing unit 9 activates the second classifier 13, and the image data determined not to belong to the first classification, that is, the image data (defect F) classified as "other" is Determine whether or not it belongs to the second category.
  • the arithmetic processing unit 9 determines in the second classifier 13 whether or not the defect F belongs to any of the sub-classifications such as "triangle", "polygon", “circle", and "white”.
  • the arithmetic processing unit 9 classifies the image data into the second classification.
  • the arithmetic processing unit 9 adds classification information related to the second classification to the image data and stores it in the storage unit 10.
  • the arithmetic processing unit 9 classifies the defect F as "other".
  • the arithmetic processing unit 9 adds the classification information to the image data and stores it in the storage unit 10.
  • the arithmetic processing unit 9 transmits the image data for which the classification has been completed to the image processing device 4.
  • the image processing device 4 stores the classified image data transmitted from the defect classification device 5 in the storage unit 7.
  • the image processing device 4 displays the image data received from the defect classification device 5 on the display 4a by the operation of the operator or by automatic control.
  • the display 4a displays the classification information (information on "bubbles", “platinum foreign matter”, and “others") together with the image data.
  • the operator visually certifies the type of defect F for the defect F classified as "other" in the first classification process. Specifically, the operator visually observes the image data displayed on the display 4a of the image processing device 4, and the type of defect F (for example, "bubble”, “platinum foreign matter”, “foreign matter other than platinum”, “foreign matter other than platinum”, “ “Dirt”, “Scratch”) is certified. The operator adds classification information (certification result) to the image data and stores it in the storage unit 7.
  • the image processing device 4 determines the quality of the glass plate G based on information such as the size (dimensions), depth D, and type of the defect F. Specifically, when the image processing apparatus 4 determines that the defect F adversely affects the surface texture of the surface S of the glass plate G, that is, the defect F causes unevenness or the like on the surface S of the glass plate G. The glass plate G corresponding to the image data is recognized as a defective product. On the other hand, the image processing apparatus 4 recognizes the glass plate G as a non-defective product when the defect F does not adversely affect the surface properties of the surface S of the glass plate G.
  • the type of the defect F included in the image data is classified by the defect classification device 5.
  • the defect classification device 5 By automatically classifying, it is possible to improve the work efficiency of the inspection step S4. Thereby, the manufacturing efficiency of the glass article can be improved.
  • the present invention is not limited to the configuration of the above embodiment, and is not limited to the above-mentioned action and effect.
  • the present invention can be modified in various ways without departing from the gist of the present invention.
  • the inspection device 1 in which the image processing device 4 and the defect classification device 5 are configured by separate devices is illustrated, but the present invention is not limited to this configuration.
  • the image processing device 4 and the defect classification device 5 may be configured by one device.
  • a method and an apparatus for classifying a defect F of a glass plate G as a glass article have been exemplified, but the present invention is not limited to this configuration.
  • the present invention is also applicable, for example, to classify defects contained in tube glass.
  • the present invention is not limited to this configuration.
  • the classification step S43 can be executed only by the defect classification device 5.
  • the first classifier 12 that classifies the types of defects into the first classification (“foam”) and the second classifier 13 that classifies the types of defects into the second classification (“platinum foreign matter”).
  • the defect classification device 5 including the above is illustrated, the present invention is not limited to this configuration.
  • the defect classification device 5 may further include, for example, one or more other classifiers that classify the types of defects into different classifications.
  • the first classification is not limited to "foam” and may be another type of defect
  • the second classification is not limited to "platinum foreign matter" and may be another type of defect.
  • the first classifier 12 and the second classifier 13 are used regardless of the depth of the defect, but the first classifier 12 and the second classifier 13 according to the depth of the defect are prepared.
  • the defects may be classified by the first classifier 12 and the second classifier 13 corresponding to the measured depth of the defect.
  • the first teacher data about "foam” located at the depth around the front surface of the glass plate
  • the first teacher data about "foam” located at the depth around the back surface of the glass plate
  • the middle between the front surface and the back surface prepare the first teacher data about the "bubble” located at the depth.
  • the first classifier for "foam” located around the front surface the first classifier for "foam” located around the back surface, and the "foam” located between the front and back surfaces. Create the first classifier for each.
  • a depth determination step for determining whether or not the defect has a predetermined depth can be performed before the first determination step S431 is executed. Specifically, in the depth determination step, it is determined whether the detected image data defect is located near the front surface, the middle, or the back surface. Then, in the subsequent first determination step S431, the type of defect is classified using the first classifier at the position corresponding to the determination result in the depth determination step. In this case, as for the second classifier, as with the first classifier, a second classifier for "platinum foreign matter" located around the front surface, the middle, and the back surface is created. In the second determination step S432, similarly to the first determination step S431, the type of defect is determined by using the second classifier at the position corresponding to the determination result in the depth determination step.
  • the present inventors conducted a test to confirm an effective machine learning method by a defect classification device in order to accurately classify the types of defects contained in image data.
  • the present inventors have created teacher data in which image data having the attributes of "bubble”, “platinum foreign matter”, “foreign matter other than platinum”, “dirt”, and “scratch” are mixed, and machine learning by a defect classification device. (Deep learning) was carried out.
  • the defect inference model (algorithm) created by this machine learning will be referred to as a comparative example.
  • the present inventors created teacher data (first teacher data) including only image data having the attribute of "bubble", and carried out machine learning by a defect classification device.
  • the defect inference model (first classifier) created by this machine learning will be referred to as Example 1.
  • Example 2 teacher data including only image data having the attribute of "platinum foreign matter", and carried out machine learning by a defect classification device.
  • the defect inference model (second classifier) created by this machine learning will be referred to as Example 2.
  • the correct answer rate of the comparative example was 100%
  • the correct answer rate of the example 1 was 100%
  • the correct answer rate of the comparative example and the correct answer rate of the example 1 were the same.
  • the correct answer rate of Comparative Example was 76%
  • the correct answer rate of Example 2 was 97%, and it was found that the correct answer rate of Example 2 was higher than the correct answer rate of Comparative Example.
  • the classification related to "bubbles” (first determination step S431) is executed first, and then.
  • the classification related to "platinum foreign matter” (second determination step S432), the classification (particularly the classification related to "platinum foreign matter” having a low accuracy rate in the comparative example) can be performed accurately and efficiently. It became clear.

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Abstract

Procédé de classification d'un défaut dans un article en verre, le procédé comprenant : une première étape d'évaluation S431 durant laquelle une évaluation est effectuée par un premier classificateur 12, sur la base de données d'image, quant à savoir si un défaut F appartient à une première classification ; et une seconde étape d'évaluation S432 durant laquelle, lorsqu'il a été évalué que le défaut F n'appartient pas à la première classification, une évaluation est effectuée par un second classificateur 13 quant à savoir si défaut F appartient à une seconde classification.
PCT/JP2020/043010 2019-12-17 2020-11-18 Procédé de classification de défaut, dispositif de classification de défaut et procédé de production d'article en verre WO2021124775A1 (fr)

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