WO2016043397A1 - Procédé et appareil de détection de défauts de verre utilisant une technique d'imagerie hyperspectrale - Google Patents

Procédé et appareil de détection de défauts de verre utilisant une technique d'imagerie hyperspectrale Download PDF

Info

Publication number
WO2016043397A1
WO2016043397A1 PCT/KR2015/003634 KR2015003634W WO2016043397A1 WO 2016043397 A1 WO2016043397 A1 WO 2016043397A1 KR 2015003634 W KR2015003634 W KR 2015003634W WO 2016043397 A1 WO2016043397 A1 WO 2016043397A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
image
defect
defects
hyperspectral
Prior art date
Application number
PCT/KR2015/003634
Other languages
English (en)
Korean (ko)
Inventor
홍진광
Original Assignee
한화테크윈 주식회사
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 한화테크윈 주식회사 filed Critical 한화테크윈 주식회사
Publication of WO2016043397A1 publication Critical patent/WO2016043397A1/fr

Links

Images

Classifications

    • 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
    • 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
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Definitions

  • the present invention relates to a glass defect detection method, and more particularly to a glass defect detection method using a hyperspectral image.
  • a plurality of images are irradiated at various angles to acquire images from a single camera, thereby obtaining images.
  • a combination of four or more images obtained from a single camera or a combination of eight to twelve images with two or more cameras disposed at the top and the bottom thereof detects defects and good products.
  • a glass defect detection method using a hyperspectral imaging method generates a hyperspectral image of glass, and the hyperspectral image is composed of spectroscopic images of different bands. Selecting at least one spectroscopic image of a band including a defect corresponding to a predetermined test item in the generated hyperspectral image; Measuring a gray value for each defect in the spectroscopic image; Processing defect detection data for each defect in the spectroscopic image; And detecting defects by using a spectroscopic image selected for each band corresponding to the inspection item, measured shade values, and processed defect detection data.
  • the present invention has the effect of accurately detecting a variety of defects in the inspection of the appearance of the glass and film. In particular, it accurately detects defects such as printing or discoloration, and accurately classifies floating foreign substances such as dust and stains, thereby maximizing inspection yield.
  • FIG. 1 illustrates a glass defect inspection apparatus for generating a hyperspectral image as an exemplary embodiment of the present invention.
  • FIG. 2 illustrates an example in which defects according to inspection items of the inspection target glass 140 are detected and spectral data for each defect is displayed.
  • FIG. 3 illustrates an embodiment in which a spectroscopic image of a band in which a defect is detected in a hyperspectral image of the inspection target glass 140 is detected as an exemplary embodiment of the present invention.
  • a defect is detected according to an inspection item of the inspection target glass 140, and the wavelength value measured for each detected defect and a preset reference value are disclosed.
  • FIG. 5 illustrates, as a preferred embodiment of the present invention, a shade value measurement value and defect detection (Blob) data of a defect detected according to an inspection item of an inspection object glass 140.
  • FIG. 6 shows an example of defect detection (Blob) data of a defect as a preferred embodiment of the present invention.
  • FIG. 7 illustrates an example of detecting a glass defect using all of image data, spectroscopic data, and spatial data as an exemplary embodiment of the present invention.
  • FIG. 8 illustrates a glass defect detection flowchart using a hyperspectral imaging technique according to an exemplary embodiment of the present invention.
  • a glass defect detection method using a hyperspectral imaging method generates a hyperspectral image of glass, and the hyperspectral image is composed of spectroscopic images of different bands. Selecting at least one spectroscopic image of a band including a defect corresponding to a predetermined test item in the generated hyperspectral image; Measuring a gray value for each defect in the spectroscopic image; Processing defect detection data for each defect in the spectroscopic image; And detecting defects by using a spectroscopic image selected for each band corresponding to the inspection item, measured shade values, and processed defect detection data.
  • the defect detection data for the defect includes at least one or more of the coordinates, the center of gravity coordinates, the area, the circumferential length, and the diagonal length of each defect.
  • the defects are classified by type using band information of a spectroscopic image in which each defect is detected.
  • a glass defect detection method using a hyperspectral imaging technique may include generating a hyperspectral image of glass using an imaging apparatus and a spectroscope; Acquiring image data, spectral data, and spatial data measurement values for the defects corresponding to each of the preset inspection items using the generated hyperspectral image; Analyzing preset image data, spectral data, and spatial data reference values and measured values of the image data, spectral data, and spatial data with respect to defects corresponding to each of the inspection items; And classifying a defect corresponding to the inspection item as defective when the image data, the spectral data, and the spatial data measurement values exceed the preset image data, the spectral data, and the spatial data reference values.
  • a glass defect detection method using a hyperspectral imaging technique includes: generating a hyperspectral image of an inspection target glass using an imaging apparatus and a spectroscope; A hyperspectral image analysis step of displaying each defect corresponding to a predetermined inspection item in the hyperspectral image as image data, spectral data and spatial data values; and using all of the displayed image data, spectral data and spatial data values Detecting whether each defect is defective.
  • FIG. 1 illustrates a glass defect inspection apparatus for generating a hyperspectral image as an exemplary embodiment of the present invention.
  • the glass defect inspection apparatus 100 includes a hyperspectral camera 102 and a hyperspectral image processing apparatus 150.
  • the hyperspectral camera 102 includes image capturing apparatuses 110 and 120, a spectrometer 130, a slit 132, and a focusing lens 134.
  • Image capturing apparatus (110, 120) is composed of a camera 110 and a detector (120).
  • the glass defect inspection apparatus 100 generates a hyperspectral image of the inspection target glass 140 by using the image photographing apparatus 110 and 120 and the spectrometer 130.
  • Hyperspectral image refers to an image having a very high spectral resolution composed of more than one hundred consecutive bands or channel-specific spectroscopic images.
  • the spectroscope 130 disperses the light incident from the optical lenses of the imaging apparatuses 110 and 120 into a spatial pixel and a spectral pixel in a hyperspectral camera using a diffraction grating.
  • Examples of the spectrometer 130 include a reflective grating diffractometer, an acoustic optical bragg grating diffractometer and a volume phase holographic grating diffractometer, and a spectral sensor of a Fabry-Perrot Filter directly applied to a detector. And does not exclude additional spectroscopic components.
  • examples of the inspection target glass 140 include a bare glass, a print glass, a touch screen pattern, a smartphone cover glass and a film.
  • the hyperspectral image provides spectroscopic information, spatial information, and image information for each pixel of the image by combining the image photographing apparatus 110 and 120 and the spectrometer 130.
  • the glass defect inspection apparatus 100 irradiates an illumination light source to the glass 140 to be inspected, and hyperspectrally distributes light information of the glass 140 that is reflected, transmitted, excited, and absorbed from the irradiated light source in the form of a hyperspectral image.
  • the image processing apparatus 150 acquires the image and displays the test result on the screen display unit.
  • the illumination light source generator may be manufactured as a light source having a short wavelength or multiple wavelengths according to the purpose of the inspection object, and may be installed at the upper and lower portions thereof and irradiate the sample.
  • an external unwanted noise light source may be incident to the hyperspectral camera, and thus, the hyperspectral image generator should be manufactured in a dark zone condition to minimize errors.
  • the hyperspectral image processing apparatus 150 may be implemented together with the glass defect inspection apparatus 100 or as a separate device capable of wired and wireless communication with the glass defect inspection apparatus 100.
  • Examples of the hyperspectral image processing apparatus 150 include a thin terminal, a handheld device, a computer, a smartphone, a notebook, and the like.
  • the hyperspectral image processing apparatus 150 may display the captured hyperspectral image as image data, spectral data, and spatial data values. In addition, the hyperspectral image processing apparatus 150 may detect a defect in the hyperspectral image by using the displayed image data, spectral data, and spatial data values. The hyperspectral image processing apparatus 150 displays the defects corresponding to the preset inspection item by using the photographed hyperspectral image as image data, spectral data, and spatial data values, and displays the displayed image data, spectral data, and spatial data values. All of the defects can be detected using the above.
  • a gray scale value may be used. From the image data of the hyperspectral image generated by the glass defect inspection apparatus 100, one or more images of spectral bands can be selected to display a joke value as a value between 0 and 255 (8 bits), and the expression of the joke value is 0 to 255. It is not limited to (8 bits). In this regard, reference is made to the description related to FIGS. 5 to 6.
  • the spectral data uses a wavelength value of the hyperspectral image.
  • the glass defect inspection apparatus 100 may reclassify the detected defects by type by using a characteristic that the detected defects have different wavelengths for each property. In this regard, reference is made to the description of FIGS. 2 to 4.
  • the spatial data may use (x, y) position or coordinate value of each pixel of the hyperspectral image.
  • coordinate values such as 100 pixels, 100 pixels or 10 mm, 10 mm may be used.
  • FIG. 2 illustrates an example in which defects according to inspection items of the inspection object glass 140 are detected and spectral data for each defect is displayed.
  • the spectral data includes wavelength information of an image for each wavelength band that is continuously obtained for each unit of spectral resolution in the spectral region. For example, when the spectral region is 400 nm to 1,000 nm and the spectral resolution is 2 nm, the spectral data can be obtained from the images obtained in the order of 400 nm, 402 nm, 404 nm, ..., 1000 nm.
  • the inspection items are print color 210, print discoloration 212, scratches 214, 234, foreign objects 216, 218, 220, 222, imprint 224, chipping 226 , Blobs 228, Mura 230, dents 232, IR or camera hole defects 236.
  • the print color 210 is lambda 1
  • the print discoloration 212 is lambda 2
  • the scratches 214 and 234 are lambda 3 in the window area, and the scratch occurs across the printing unit and the window area.
  • the spectral data value was measured as ⁇ 11.
  • the spectral data values of the dust 216 were ⁇ 4, the hair 218 was ⁇ 5, the salinity was (220) ⁇ 6, and the conveyor belt 222 was ⁇ 7.
  • the image 224 is ⁇ 8, the chipping 226 is ⁇ 9, the stain 228 is ⁇ 10, the Mura 230 is ⁇ 13, the dent 232 is ⁇ 12, and IR or camera hole defects. (236) was measured for spectral data as ⁇ 14.
  • FIG. 3 illustrates an embodiment of reclassifying the defects detected by the glass defect inspection apparatus (FIGS. 1 and 100) according to a preferred embodiment of the present invention.
  • the glass defect inspection apparatus may reclassify the hyperspectral image generated for the inspection target glass 310 for each type of detected defect.
  • the glass defect inspection apparatus may classify an image by material and display the hyperspectral image composed of images captured by one or more consecutive bands or channels (310, 312, 314, 316). , 318, 320, 330 and 340).
  • a defect of bare glass when having a wavelength of ⁇ 1 310 For example, a defect of bare glass when having a wavelength of ⁇ 1 310, a defect of printing when having a wavelength of ⁇ 2 312, a defect of printing when having a wavelength of ⁇ 2 312, and a floating foreign material defect when having a wavelength of ⁇ 3 to ⁇ 4 (314, 316).
  • the wavelength is ⁇ 5 (318), it is a fixed foreign matter defect, if it has a wavelength of ⁇ 6 (320), IR or a camera hole defect, if it has a wavelength of ⁇ 7 (322), the print blot defect, ⁇ 6 (320)
  • it is classified as a chipping defect in the case of having a wavelength distributed in the range of ⁇ 10 to ⁇ 30 330, and as a scratch in the case of having a wavelength distributed in the range of ⁇ 20 to ⁇ 40 340.
  • the criteria of defect classification for each wavelength ⁇ are not limited thereto, and may be applied in various combinations.
  • FIG. 4 illustrates an embodiment in which spectroscopic data of defects detected in a hyperspectral image of a glass to be inspected is measured as an exemplary embodiment of the present invention.
  • the hyperspectral image 400 of the smartphone cover glass is largely divided into the print area 401 and the window area 402.
  • the inspection item of the print area 401 includes an IR hole 410, a print color 412, a color change 414, and a scratch 416.
  • Inspection items of the window area 402 include dust 418, conveyor belt 420, stain 422, scratch 424, and stamp 426.
  • the IR hole 410 is 630 nm
  • the printing color 412 is 455 nm
  • the printing discoloration 414 is 480 nm
  • the scratch 416 is The wavelength value of 495 nm was measured.
  • the wavelength measurement value 430 measured according to each inspection item exceeds the wavelength reference value 440 and the wavelength error range 450.
  • the wavelength measurement value 430 is 480 nm to 484 nm, while the wavelength reference value is 451 nm and the wavelength error is +/ ⁇ . 5 nm appears to exceed the tolerance.
  • the print discoloration 414 inspection item is detected as a defect.
  • Figure 5 is a preferred embodiment of the present invention, as a preferred embodiment of the present invention, measured by inspection items (510, 512, 514, 516, 518, 520, 522, 524, 526) of the inspection target glass
  • the measurement values 530 and 550 of the image data and the spatial data and the predetermined spatial data reference value 552 for each inspection item are shown.
  • FIG. 6 shows defect detection (Blob) data of the spot 522 detected in the window region 502 of FIG.
  • Defect detection (Blob) data includes the coordinates (x, y) of the inspection item, the center of gravity (600), the area size, the length of the diagonal (S600) and the length of the circumference (S610).
  • defect detection data for defects in the processed hyperspectral image may be extracted and used.
  • image processing is performed to detect defects, among which a smoothing filter (averaging, Gaussian, etc.) for removing noise, an edge filter (Sobel filter, Canny filter) for contour detection, a morphological morphology processing technique (Erosion, swelling, removal, filling, etc.), defect detection functions (number of defects, location, center, area, contour information, etc.), image binarization (single binarization, dynamic binarization)
  • a smoothing filter averaging, Gaussian, etc.
  • an edge filter Sobel filter, Canny filter
  • morphological morphology processing technique Erosion, swelling, removal, filling, etc.
  • defect detection functions number of defects, location, center, area, contour information, etc.
  • image binarization single binarization, dynamic binarization
  • the present invention is not limited thereto and may include all techniques for recognizing a defect of an object in an image.
  • each spectroscopic image in which a defect is detected is extracted at least or more (FIG. 3, 310, 312, 314, 316, 318, 320, 330 and 340. Thereafter, it is determined whether the wavelength of the defect detected in each spectroscopic image exceeds a predetermined range, and primarily, whether the defect belongs to the defect.
  • the gray value of the detected defect is measured as in the exemplary embodiment illustrated in FIG. 5 (530).
  • the image is smoothed using a smoothing filter
  • the contour detection filter is used to detect the contour of the test object
  • the morphological morphology processing technique is used to express the morphological aspects of the image and perform various mathematical operations.
  • the GV binary value 540 may be performed through a binarization filter according to a purpose before or after performing the above various image processing.
  • the image processing method may be variously modified according to the purpose of detecting a defect, and the present invention may not be limited thereto and may include all techniques for recognizing a defect of an object in an image.
  • the defect detection data 550 may be derived using the GV binary value 540 and the defect detection function calculated in FIG. 5.
  • FIG. 7 illustrates an example of detecting a defect using all of spectral data, image data, and spatial data.
  • the spectral image is selected using a spectroscopic data including a defect to be detected in the hyperspectral image of the inspection target (see FIG. 3). Thereafter, the gray value of the defect is measured using image data of the defect in the selected spectroscopic image (see FIG. 5). Thereafter, defect detection data is extracted from the binarized image obtained by binarizing the measured gray value using a defect detection function of a defect (see FIG. 5).
  • a spectroscopic image including a defect corresponding to the inspection item is selected.
  • a defect corresponding to a predetermined inspection item is detected in each of the print area 701 and the window area 702 in the selected spectroscopic image.
  • Examples of preset inspection items include IR 710, color 712, color change 714, scratch 716, foreign material 1 718 in the window area 702, and the like. There is foreign material 2 720, stain 722, scratch 724 and chipping 726.
  • the bands 730 in which the IR 710 defects are detected are 630 nm, 632 nm, and 634 m, and the shaded values 740 are 51, 55, and 80 in each band.
  • the bands in which the color 712 defect was detected were 450 nm and 452 nm, and the shaded value 740 was measured as 0 and 0 in each band.
  • Discoloration 714 was detected in the areas of 454nm and 458nm, and the shade value 740 was measured as 10 and 15 in each band.
  • the shade value 740 was measured as 10 and 15 in each band.
  • the scratch 716 the foreign material 1 718, the foreign material 2 720, the stain 722, the scratch 724 and the chipping 726, the detected band and the shade value 740 in each band, respectively.
  • the spectral data and the image data obtained by the above method are then subjected to image processing (742, 744, 746).
  • image processing include at least one of image processing using a filter, mathematical and logical operation processing, and binarization processing.
  • the defect detection data of the defect is calculated using the gray value 740 value and the defect detection function (Blob) (750).
  • the defect detection data refer to the description of FIGS. 5 to 6.
  • the spatial data of the defect detection data it is determined whether the total number of defects for each band of the image, the individual positions of the defects, the individual centers, the individual areas, and the individual contour information pass the preset criteria 760.
  • the above process compares or calculates the difference with the predetermined standard and classifies it as good or bad, rework, etc. (S780).
  • FIG. 8 is a flowchart illustrating a glass defect detection using a hyperspectral imaging technique according to a preferred embodiment of the present invention.
  • the glass defect inspection apparatus (FIGS. 1 and 100) of the present invention generates a hyperspectral image of the inspection target glass by using a hyperspectral imaging camera (FIGS. 1 and 102) (S800).
  • a spectroscopic image (see FIG. 3) of a band including a defect corresponding to a predetermined test item is selected (S810).
  • one or more spectroscopic images may be selected.
  • the gray value of the defect is measured in the selected spectral image (S820). Thereafter, the selected spectral image is binarized by performing mathematical and logical operations or image capturing methods, and defect detection data for defects in the binarized hyperspectral image is calculated (S830).
  • the present invention can also be embodied as computer readable code on a computer readable recording medium.
  • the computer-readable recording medium includes all kinds of recording devices in which data that can be read by a computer system is stored.
  • Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disks, optical data storage devices, and the like.
  • the computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
  • Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)

Abstract

L'invention porte sur un appareil de détection de défauts de verre utilisant une technique d'imagerie hyperspectrale. L'appareil de détection de défauts de verre comprend : une unité de génération d'image hyperspectrale pour générer une image hyperspectrale de verre à inspecter, à l'aide d'un dispositif de photographie d'image et d'un spectroscope ; une unité d'analyse d'image hyperspectrale pour afficher chaque défaut correspondant à un élément d'inspection prédéterminé, tel que des données d'image, des données spectroscopiques et des valeurs de données spatiales, à l'aide de l'image hyperspectrale ; et une unité de détection de défaut pour détecter un défaut parmi les défauts à l'aide de la totalité des données d'image, des données spectroscopiques et des valeurs de données spatiales affichées.
PCT/KR2015/003634 2014-09-16 2015-04-10 Procédé et appareil de détection de défauts de verre utilisant une technique d'imagerie hyperspectrale WO2016043397A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR10-2014-0122929 2014-09-16
KR1020140122929A KR102003781B1 (ko) 2014-09-16 2014-09-16 초분광영상화 기법을 이용한 글라스(Glass) 결함 검출 장치

Publications (1)

Publication Number Publication Date
WO2016043397A1 true WO2016043397A1 (fr) 2016-03-24

Family

ID=55533418

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2015/003634 WO2016043397A1 (fr) 2014-09-16 2015-04-10 Procédé et appareil de détection de défauts de verre utilisant une technique d'imagerie hyperspectrale

Country Status (2)

Country Link
KR (1) KR102003781B1 (fr)
WO (1) WO2016043397A1 (fr)

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109001213A (zh) * 2018-07-19 2018-12-14 华南理工大学 一种卷到卷超薄柔性ic基板外观检测方法
EP3534151A1 (fr) * 2018-03-02 2019-09-04 Seiko Epson Corporation Appareil, système et procédé d'inspection
CN111445452A (zh) * 2020-03-23 2020-07-24 Oppo（重庆）智能科技有限公司 电子产品的缺陷检测方法、装置及计算机可读存储介质
CN111693546A (zh) * 2020-06-16 2020-09-22 湖南大学 缺陷检测***、方法及图像采集***
CN112465741A (zh) * 2020-10-10 2021-03-09 湖南大捷智能装备有限公司 悬架弹簧和气门弹簧的缺陷检测方法、装置及存储介质
US11249032B2 (en) 2017-11-15 2022-02-15 Corning Incorporated Methods and apparatus for detecting surface defects on glass sheets
CN114466183A (zh) * 2022-02-21 2022-05-10 江东电子材料有限公司 基于特征光谱的铜箔瑕疵检测方法、装置和电子设备
CN115880301A (zh) * 2023-03-06 2023-03-31 长沙韶光芯材科技有限公司 一种玻璃基板气泡缺陷的识别***
CN116297497A (zh) * 2023-05-23 2023-06-23 武汉大学 一种基于高光谱遥感的手机面板质量检测方法

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3053126B1 (fr) * 2016-06-27 2019-07-26 Saint-Gobain Glass France Procede et dispositif de localisation de l'origine d'un defaut affectant un empilement de couches minces deposees sur un substrat
KR101958539B1 (ko) * 2017-01-17 2019-03-14 서울시립대학교 산학협력단 초분광 이미지를 이용하여 콘크리트 상태를 판정하는 장치 및 방법
WO2022085822A1 (fr) * 2020-10-22 2022-04-28 (주)레이텍 Dispositif d'inspection hyperspectrale permettant de détecter une substance étrangère souple

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030218743A1 (en) * 2002-05-13 2003-11-27 Klaus Gerstner Method and apparatus for detecting defects in a continuously moving strip of transparent material
KR20080060041A (ko) * 2006-12-26 2008-07-01 엘지디스플레이 주식회사 액정 표시장치의 얼룩 검사 방법
KR20120133734A (ko) * 2011-06-01 2012-12-11 주식회사 나노텍 유리기판 에지부의 결함유무 검출을 위한 장치 및 방법
KR20140012339A (ko) * 2012-07-19 2014-02-03 (주) 인텍플러스 디스플레이 패널 검사장치
KR20140016313A (ko) * 2011-03-21 2014-02-07 페더럴-모걸 코오포레이숀 다중 분광 촬상 시스템 및 그것에 의한 표면 검사 방법

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013061185A (ja) 2011-09-12 2013-04-04 Toshiba Corp パターン検査装置およびパターン検査方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030218743A1 (en) * 2002-05-13 2003-11-27 Klaus Gerstner Method and apparatus for detecting defects in a continuously moving strip of transparent material
KR20080060041A (ko) * 2006-12-26 2008-07-01 엘지디스플레이 주식회사 액정 표시장치의 얼룩 검사 방법
KR20140016313A (ko) * 2011-03-21 2014-02-07 페더럴-모걸 코오포레이숀 다중 분광 촬상 시스템 및 그것에 의한 표면 검사 방법
KR20120133734A (ko) * 2011-06-01 2012-12-11 주식회사 나노텍 유리기판 에지부의 결함유무 검출을 위한 장치 및 방법
KR20140012339A (ko) * 2012-07-19 2014-02-03 (주) 인텍플러스 디스플레이 패널 검사장치

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11249032B2 (en) 2017-11-15 2022-02-15 Corning Incorporated Methods and apparatus for detecting surface defects on glass sheets
US11209313B2 (en) 2018-03-02 2021-12-28 Seiko Epson Corporation Inspection apparatus, inspection system, and inspection method
US10837831B2 (en) 2018-03-02 2020-11-17 Seiko Epson Corporation Inspection apparatus, inspection system, and inspection method
EP3534151A1 (fr) * 2018-03-02 2019-09-04 Seiko Epson Corporation Appareil, système et procédé d'inspection
CN109001213A (zh) * 2018-07-19 2018-12-14 华南理工大学 一种卷到卷超薄柔性ic基板外观检测方法
CN109001213B (zh) * 2018-07-19 2021-08-10 华南理工大学 一种卷到卷超薄柔性ic基板外观检测方法
CN111445452B (zh) * 2020-03-23 2022-03-01 Oppo（重庆）智能科技有限公司 电子产品的缺陷检测方法、装置及计算机可读存储介质
CN111445452A (zh) * 2020-03-23 2020-07-24 Oppo（重庆）智能科技有限公司 电子产品的缺陷检测方法、装置及计算机可读存储介质
WO2021253482A1 (fr) * 2020-06-16 2021-12-23 湖南大学 Système de détection de défauts, procédé et système de capture d'images
CN111693546A (zh) * 2020-06-16 2020-09-22 湖南大学 缺陷检测***、方法及图像采集***
CN112465741A (zh) * 2020-10-10 2021-03-09 湖南大捷智能装备有限公司 悬架弹簧和气门弹簧的缺陷检测方法、装置及存储介质
CN112465741B (zh) * 2020-10-10 2024-03-26 湖南大捷智能装备有限公司 悬架弹簧和气门弹簧的缺陷检测方法、装置及存储介质
CN114466183A (zh) * 2022-02-21 2022-05-10 江东电子材料有限公司 基于特征光谱的铜箔瑕疵检测方法、装置和电子设备
CN115880301A (zh) * 2023-03-06 2023-03-31 长沙韶光芯材科技有限公司 一种玻璃基板气泡缺陷的识别***
CN115880301B (zh) * 2023-03-06 2023-06-06 长沙韶光芯材科技有限公司 一种玻璃基板气泡缺陷的识别***
CN116297497A (zh) * 2023-05-23 2023-06-23 武汉大学 一种基于高光谱遥感的手机面板质量检测方法
CN116297497B (zh) * 2023-05-23 2023-08-15 武汉大学 一种基于高光谱遥感的手机面板质量检测方法

Also Published As

Publication number Publication date
KR20160032593A (ko) 2016-03-24
KR102003781B1 (ko) 2019-07-25

Similar Documents

Publication Publication Date Title
WO2016043397A1 (fr) Procédé et appareil de détection de défauts de verre utilisant une technique d'imagerie hyperspectrale
JP6629455B2 (ja) 外観検査装置、照明装置、撮影照明装置
US6690464B1 (en) High-volume on-line spectroscopic composition testing of manufactured pharmaceutical dosage units
KR101338576B1 (ko) 화상 해석에 의해서 결함 검사를 실시하는 결함검사장치
Boukouvalas et al. ASSIST: automatic system for surface inspection and sorting of tiles
JP2006189421A (ja) 高知能デジタルイメージ検査システム及びその検査方法
CN103954634A (zh) 一种印刷品在线质量检测***
EP4200797A1 (fr) Système de détection de défaut de surface
US20100039510A1 (en) Method and DEVICE for PRINT INSPECTION
US20060066846A1 (en) Apparatus and method for detection of contaminant particles or component defects
WO2015126111A1 (fr) Appareil et procédé d'essai de conductivité de graphène
CN112666180B (zh) 一种点胶自动化检测方法及***
Boukouvalas et al. An integrated system for quality inspection of tiles
KR101536693B1 (ko) 다파장 영상구현 및 영상분석을 통한 흑색종판별방법 및 흑색종판별시스템
JP5727709B2 (ja) 残留物測定方法及び残留物測定装置
JP2009264876A (ja) 製品品質の検査システム及びその方法
US20120268588A1 (en) Method and system for determining optical properties of a lens system
JP2007271507A (ja) 欠陥検出方法、欠陥検出装置、及び欠陥検出プログラム
JPH1023203A (ja) 紙分類装置
JP7411155B2 (ja) 色ムラ検査装置および色ムラ検査方法
JP2006300615A (ja) 外観検査装置及び外観検査方法
JP7136064B2 (ja) 被検査体の表面検査装置および被検査体の表面検査方法
JP2020187093A (ja) 撮影方法及び撮影システム
WO2021040409A1 (fr) Procédé d'inspection optique et appareil correspondant
CN219799264U (zh) 一种分光分色瑕疵检测装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15842288

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 15842288

Country of ref document: EP

Kind code of ref document: A1