MX2021007733A - Metodo de generacion de modelo aprendido, modelo aprendido, metodo de inspeccion de defectos de superficie, metodo de fabricacion de acero, metodo de determinacion de pasa/no pasa, metodo de determinacion de grado, programa de determinacion de defectos de superficie, programa de determinacion de pasa/no pasa, sistema de determinacion y equipo de fabricacion de acero. - Google Patents

Metodo de generacion de modelo aprendido, modelo aprendido, metodo de inspeccion de defectos de superficie, metodo de fabricacion de acero, metodo de determinacion de pasa/no pasa, metodo de determinacion de grado, programa de determinacion de defectos de superficie, programa de determinacion de pasa/no pasa, sistema de determinacion y equipo de fabricacion de acero.

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Publication number
MX2021007733A
MX2021007733A MX2021007733A MX2021007733A MX2021007733A MX 2021007733 A MX2021007733 A MX 2021007733A MX 2021007733 A MX2021007733 A MX 2021007733A MX 2021007733 A MX2021007733 A MX 2021007733A MX 2021007733 A MX2021007733 A MX 2021007733A
Authority
MX
Mexico
Prior art keywords
defect
learned
generation method
model generation
learned model
Prior art date
Application number
MX2021007733A
Other languages
English (en)
Inventor
Hiroaki Ono
Takahiro Koshihara
Original Assignee
Jfe Steel Corp
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 Jfe Steel Corp filed Critical Jfe Steel Corp
Publication of MX2021007733A publication Critical patent/MX2021007733A/es

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Classifications

    • 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
    • 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
    • G06T7/001—Industrial image inspection using an image reference approach
    • 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
    • G01N21/8922—Periodic flaws
    • 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
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • 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
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • 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
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • 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
    • G01N2021/8883—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 involving the calculation of gauges, generating models
    • 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
    • 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
    • G01N21/8914—Investigating the presence of flaws or contamination in moving material, e.g. running paper or textiles characterised by the material examined
    • G01N2021/8918—Metal
    • 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
    • G01N2021/8924—Dents; Relief flaws
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/048—Activation functions
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/06—Recognition of objects for industrial automation

Abstract

Se genera un método de generación de modelo aprendido, usando una imagen maestra que es una imagen que indica una distribución de una porción defectuosa de una superficie de acero e incluye un mapa de defectos de un tamaño de imagen igual y la presencia/ausencia de defectos periódicos asignados de antemano para el mapa de defectos relevante, un modelo aprendido para lo cual un mapa de defectos que es una imagen que indica una distribución de una porción defectuosa de una superficie de acero y que tiene un tamaño de imagen del tamaño de imagen igual es un valor de entrada y un valor relacionado con la presencia/ausencia de defectos periódicos en el mapa de defectos relevante es un valor de salida, por aprendizaje de máquina.
MX2021007733A 2018-12-25 2019-10-31 Metodo de generacion de modelo aprendido, modelo aprendido, metodo de inspeccion de defectos de superficie, metodo de fabricacion de acero, metodo de determinacion de pasa/no pasa, metodo de determinacion de grado, programa de determinacion de defectos de superficie, programa de determinacion de pasa/no pasa, sistema de determinacion y equipo de fabricacion de acero. MX2021007733A (es)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2018241213 2018-12-25
PCT/JP2019/042848 WO2020137151A1 (ja) 2018-12-25 2019-10-31 学習済みモデルの生成方法、学習済みモデル、表面欠陥検出方法、鋼材の製造方法、合否判定方法、等級判定方法、表面欠陥判定プログラム、合否判定プログラム、判定システム、及び鋼材の製造設備

Publications (1)

Publication Number Publication Date
MX2021007733A true MX2021007733A (es) 2021-08-05

Family

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Family Applications (1)

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MX2021007733A MX2021007733A (es) 2018-12-25 2019-10-31 Metodo de generacion de modelo aprendido, modelo aprendido, metodo de inspeccion de defectos de superficie, metodo de fabricacion de acero, metodo de determinacion de pasa/no pasa, metodo de determinacion de grado, programa de determinacion de defectos de superficie, programa de determinacion de pasa/no pasa, sistema de determinacion y equipo de fabricacion de acero.

Country Status (7)

Country Link
US (1) US20220044383A1 (es)
EP (1) EP3904868A4 (es)
JP (1) JP6973623B2 (es)
KR (1) KR102636470B1 (es)
CN (1) CN113260854A (es)
MX (1) MX2021007733A (es)
WO (1) WO2020137151A1 (es)

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DE102021122939B4 (de) 2021-09-06 2023-06-01 Bayerische Motoren Werke Aktiengesellschaft Verfahren zum Beurteilen einer Oberfläche eines Karosseriebauteils sowie Verfahren zum Trainieren eines künstlichen neuronalen Netzes
KR20230073720A (ko) 2021-11-19 2023-05-26 부산대학교 산학협력단 딥러닝 모델을 이용한 냉연강판에서의 표면결함 자동분류를 위한 장치 및 방법
KR102471441B1 (ko) * 2021-12-20 2022-11-28 주식회사 아이코어 딥 러닝을 기반으로 고장을 검출하는 비전 검사 시스템

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Also Published As

Publication number Publication date
WO2020137151A1 (ja) 2020-07-02
EP3904868A1 (en) 2021-11-03
JPWO2020137151A1 (ja) 2021-02-18
CN113260854A (zh) 2021-08-13
KR20210091309A (ko) 2021-07-21
JP6973623B2 (ja) 2021-12-01
US20220044383A1 (en) 2022-02-10
EP3904868A4 (en) 2023-01-25
KR102636470B1 (ko) 2024-02-13

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