MX2019011283A - Metodo de inspeccion de defectos superficiales y aparato de inspeccion de defectos superficiales. - Google Patents
Metodo de inspeccion de defectos superficiales y aparato de inspeccion de defectos superficiales.Info
- Publication number
- MX2019011283A MX2019011283A MX2019011283A MX2019011283A MX2019011283A MX 2019011283 A MX2019011283 A MX 2019011283A MX 2019011283 A MX2019011283 A MX 2019011283A MX 2019011283 A MX2019011283 A MX 2019011283A MX 2019011283 A MX2019011283 A MX 2019011283A
- Authority
- MX
- Mexico
- Prior art keywords
- abnormality
- image
- defect inspection
- surface defect
- texture
- Prior art date
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Classifications
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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
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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
-
- 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
- G01N21/89—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles
- G01N21/892—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles characterised by the flaw, defect or object feature examined
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/89—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles
- G01N21/892—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles characterised by the flaw, defect or object feature examined
- G01N21/898—Irregularities in textured or patterned surfaces, e.g. textiles, wood
- G01N21/8983—Irregularities in textured or patterned surfaces, e.g. textiles, wood for testing textile webs, i.e. woven material
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2433—Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
- G06T7/41—Analysis of texture based on statistical description of texture
- G06T7/44—Analysis of texture based on statistical description of texture using image operators, e.g. filters, edge density metrics or local histograms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
- G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/758—Involving statistics of pixels or of feature values, e.g. histogram matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30124—Fabrics; Textile; Paper
-
- 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
- G06T2207/30136—Metal
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Multimedia (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Biochemistry (AREA)
- Pathology (AREA)
- Immunology (AREA)
- Analytical Chemistry (AREA)
- Chemical & Material Sciences (AREA)
- Textile Engineering (AREA)
- Databases & Information Systems (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Computing Systems (AREA)
- Quality & Reliability (AREA)
- Signal Processing (AREA)
- Data Mining & Analysis (AREA)
- Biodiversity & Conservation Biology (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Wood Science & Technology (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- General Engineering & Computer Science (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
En un aparato de inspección de defectos superficiales 1 de conformidad con una modalidad de la presente invención, una unidad de generación de imágenes de características de textura 43 genera una pluralidad de imágenes de características de textura mediante la aplicación de un proceso de filtrado que utiliza una pluralidad de filtros espaciales a una imagen de entrada; una unidad de extracción de características de textura 44 genera un vector de características en cada posición de la imagen, mediante la extracción de un valor en una posición correspondiente de cada una de las imágenes de características de textura, para cada una de las posiciones de la imagen de entrada; una unidad de cálculo de nivel de anomalía 45 genera una imagen de nivel de anomalía que representa un nivel de anomalía para cada posición de la imagen de entrada, mediante el cálculo de un nivel de anomalía, para cada uno de los vectores de características, en una distribución multidimensional formada por los vectores de características; y una unidad de detección de candidatos a defectos 46 detecta una parte que tiene el nivel de anomalía que es más alto que un nivel predeterminado en la imagen de nivel de anomalía como una porción de defecto o una porción de candidato a defecto.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2017054426A JP6358351B1 (ja) | 2017-03-21 | 2017-03-21 | 表面欠陥検査方法及び表面欠陥検査装置 |
PCT/JP2018/007418 WO2018173660A1 (ja) | 2017-03-21 | 2018-02-28 | 表面欠陥検査方法及び表面欠陥検査装置 |
Publications (1)
Publication Number | Publication Date |
---|---|
MX2019011283A true MX2019011283A (es) | 2019-11-01 |
Family
ID=62904948
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
MX2019011283A MX2019011283A (es) | 2017-03-21 | 2018-02-28 | Metodo de inspeccion de defectos superficiales y aparato de inspeccion de defectos superficiales. |
Country Status (7)
Country | Link |
---|---|
US (1) | US10859507B2 (es) |
EP (1) | EP3605072A4 (es) |
JP (1) | JP6358351B1 (es) |
KR (1) | KR102257734B1 (es) |
CN (1) | CN110431404B (es) |
MX (1) | MX2019011283A (es) |
WO (1) | WO2018173660A1 (es) |
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WO2018221605A1 (en) * | 2017-05-31 | 2018-12-06 | Nipro Corporation | Method for evaluation of glass container |
US11378944B2 (en) * | 2017-10-10 | 2022-07-05 | Nec Corporation | System analysis method, system analysis apparatus, and program |
JP6658711B2 (ja) * | 2017-10-25 | 2020-03-04 | Jfeスチール株式会社 | 表面欠陥検出方法及び表面欠陥検出装置 |
CN108823765B (zh) * | 2018-08-13 | 2023-08-29 | 何辉 | 智能布面监测系统 |
CN109360186A (zh) * | 2018-08-31 | 2019-02-19 | 广州超音速自动化科技股份有限公司 | 锂电池隔膜检测方法、电子设备、存储介质及系统 |
JP6950811B2 (ja) * | 2018-09-28 | 2021-10-13 | Jfeスチール株式会社 | 金属板の表面欠陥検出方法及び装置並びにめっき鋼板の製造方法 |
JP7167615B2 (ja) * | 2018-10-05 | 2022-11-09 | コニカミノルタ株式会社 | 画像検査装置、画像検査方法及び画像検査プログラム |
CN109557101B (zh) * | 2018-12-29 | 2023-11-17 | 桂林电子科技大学 | 一种非标高反射曲面工件的缺陷检测装置及方法 |
JP6630912B1 (ja) * | 2019-01-14 | 2020-01-15 | 株式会社デンケン | 検査装置及び検査方法 |
EP3938733A4 (en) * | 2019-03-15 | 2023-03-22 | Certainteed Gypsum, Inc. | SURFACE TEXTURE CHARACTERIZATION METHOD AND TEXTURE CHARACTERIZATION TOOL |
US11087449B2 (en) * | 2019-10-24 | 2021-08-10 | KLA Corp. | Deep learning networks for nuisance filtering |
CN111141753A (zh) * | 2019-12-20 | 2020-05-12 | 三峡大学 | 基于机器视觉的陶瓷瓦表面裂纹检测方法 |
CN111161246B (zh) * | 2019-12-30 | 2024-05-14 | 歌尔股份有限公司 | 一种产品缺陷检测方法、装置与系统 |
JP7237872B2 (ja) * | 2020-02-14 | 2023-03-13 | 株式会社東芝 | 検査装置、検査方法、及びプログラム |
JP7273748B2 (ja) * | 2020-02-28 | 2023-05-15 | 株式会社東芝 | 検査装置、検査方法、及びプログラム |
CN113313638A (zh) * | 2020-12-23 | 2021-08-27 | 深圳市杰恩世智能科技有限公司 | 一种外观缺陷检测方法 |
CN113030422B (zh) * | 2021-03-02 | 2022-12-16 | 成都积微物联电子商务有限公司 | 基于表检仪检测的冷轧带钢质量判定的方法 |
CN112686896B (zh) * | 2021-03-12 | 2021-07-06 | 苏州鼎纳自动化技术有限公司 | 基于分割网络的频域空间结合的玻璃缺陷检测方法 |
KR20230089381A (ko) * | 2021-12-13 | 2023-06-20 | 엘지디스플레이 주식회사 | 표시결함 검출 시스템 및 그 검출 방법 |
FR3134181A1 (fr) | 2022-04-01 | 2023-10-06 | Psa Automobiles Sa | Procede de detection d’un defaut d’etat de surface sur une surface metallique d’un element de vehicule |
KR102439163B1 (ko) * | 2022-06-24 | 2022-09-01 | 주식회사 아이브 | 인공지능 기반의 비지도 학습 모델을 이용한 불량 제품 검출 장치 및 그 제어방법 |
JP2024013523A (ja) * | 2022-07-20 | 2024-02-01 | 株式会社ニューフレアテクノロジー | 検査装置及び検査画像の生成方法 |
EP4357765A4 (en) * | 2022-08-30 | 2024-05-08 | Contemporary Amperex Technology Co Ltd | ERROR DETECTION METHOD AND APPARATUS AND COMPUTER-READABLE STORAGE MEDIUM |
JP2024048292A (ja) * | 2022-09-27 | 2024-04-08 | Jfeスチール株式会社 | 表面欠陥検出方法及び表面欠陥検出装置 |
WO2024101940A1 (ko) * | 2022-11-10 | 2024-05-16 | 주식회사 고영테크놀러지 | 결함의 유형을 결정하기 위한 장치, 방법 및 명령을 기록한 기록 매체 |
CN115641337B (zh) * | 2022-12-23 | 2023-04-07 | 中科慧远视觉技术(北京)有限公司 | 一种线状缺陷检测方法、装置、介质、设备及系统 |
CN116309578B (zh) * | 2023-05-19 | 2023-08-04 | 山东硅科新材料有限公司 | 一种应用硅烷偶联剂的塑料耐磨性图像辅助检测方法 |
CN116934746B (zh) * | 2023-09-14 | 2023-12-01 | 常州微亿智造科技有限公司 | 划伤缺陷检测方法、系统、设备及其介质 |
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CN103234976B (zh) * | 2013-04-03 | 2015-08-05 | 江南大学 | 基于Gabor变换的经编机布匹瑕疵在线视觉检测方法 |
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JP2015041164A (ja) * | 2013-08-20 | 2015-03-02 | キヤノン株式会社 | 画像処理装置、画像処理方法およびプログラム |
CN104458755B (zh) * | 2014-11-26 | 2017-02-22 | 吴晓军 | 一种基于机器视觉的多类型材质表面缺陷检测方法 |
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2017
- 2017-03-21 JP JP2017054426A patent/JP6358351B1/ja active Active
-
2018
- 2018-02-28 CN CN201880018467.5A patent/CN110431404B/zh active Active
- 2018-02-28 MX MX2019011283A patent/MX2019011283A/es unknown
- 2018-02-28 EP EP18770965.4A patent/EP3605072A4/en active Pending
- 2018-02-28 WO PCT/JP2018/007418 patent/WO2018173660A1/ja unknown
- 2018-02-28 US US16/495,228 patent/US10859507B2/en active Active
- 2018-02-28 KR KR1020197027127A patent/KR102257734B1/ko active IP Right Grant
Also Published As
Publication number | Publication date |
---|---|
CN110431404B (zh) | 2022-05-27 |
JP2018155690A (ja) | 2018-10-04 |
EP3605072A4 (en) | 2020-04-08 |
KR20190118627A (ko) | 2019-10-18 |
EP3605072A1 (en) | 2020-02-05 |
KR102257734B1 (ko) | 2021-05-27 |
CN110431404A (zh) | 2019-11-08 |
WO2018173660A1 (ja) | 2018-09-27 |
US20200025690A1 (en) | 2020-01-23 |
JP6358351B1 (ja) | 2018-07-18 |
US10859507B2 (en) | 2020-12-08 |
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