[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

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.

Info

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

Links

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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Chemical & Material Sciences (AREA)
  • Pathology (AREA)
  • Immunology (AREA)
  • Biochemistry (AREA)
  • Analytical Chemistry (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • General Engineering & Computer Science (AREA)
  • Textile Engineering (AREA)
  • Data Mining & Analysis (AREA)
  • Medical Informatics (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Signal Processing (AREA)
  • Quality & Reliability (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Multimedia (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Databases & Information Systems (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

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

ID=71127917

Family Applications (1)

Application Number Title Priority Date Filing Date
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)

Families Citing this family (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112275807B (zh) * 2020-09-30 2022-11-18 首钢集团有限公司 一种热轧带钢轮廓边部平台的检测方法及装置
JP2023007260A (ja) * 2021-07-01 2023-01-18 株式会社日立製作所 計算機及び外観検査方法
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
JP2023042738A (ja) * 2021-09-15 2023-03-28 株式会社日立ハイテク 欠陥検査システム及び欠陥検査方法
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
CN118552620A (zh) * 2024-07-30 2024-08-27 爱睿思(厦门)科技有限公司 一种基于相机内外参的图纹转印位置偏差的矫正方法
CN118735815A (zh) * 2024-09-02 2024-10-01 山东省盈鑫彩钢有限公司 一种用于冷轧钢板缺陷图像的增强处理方法

Family Cites Families (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS58156842A (ja) 1982-03-15 1983-09-17 Toshiba Corp ロ−ル疵検出装置
JPH07198627A (ja) 1994-01-06 1995-08-01 Nippon Steel Corp 金属表面欠陥検査装置
JP3507695B2 (ja) * 1998-04-30 2004-03-15 日本電信電話株式会社 カオスニューラルネットワークを用いた信号検出方法及び装置
US6625515B2 (en) * 2000-12-21 2003-09-23 Dofasco Inc. Roll defect management process
KR100838723B1 (ko) * 2001-12-05 2008-06-16 주식회사 포스코 스트립표면 결함부의 검출 및 평점산출장치
US6950546B2 (en) * 2002-12-03 2005-09-27 Og Technologies, Inc. Apparatus and method for detecting surface defects on a workpiece such as a rolled/drawn metal bar
US7324681B2 (en) * 2002-12-03 2008-01-29 Og Technologies, Inc. Apparatus and method for detecting surface defects on a workpiece such as a rolled/drawn metal bar
US7460703B2 (en) * 2002-12-03 2008-12-02 Og Technologies, Inc. Apparatus and method for detecting surface defects on a workpiece such as a rolled/drawn metal bar
JP4414658B2 (ja) * 2003-02-14 2010-02-10 株式会社メック 欠陥検査装置および欠陥検査方法
US7333650B2 (en) * 2003-05-29 2008-02-19 Nidek Co., Ltd. Defect inspection apparatus
JP2004354250A (ja) * 2003-05-29 2004-12-16 Nidek Co Ltd 欠陥検査装置
JP5453861B2 (ja) 2008-03-31 2014-03-26 Ｊｆｅスチール株式会社 周期性欠陥検出装置及びその方法
JP2010139317A (ja) * 2008-12-10 2010-06-24 Mitsubishi Materials Corp 軸物工具表面の欠陥検査方法および装置
JP5733879B2 (ja) * 2008-12-24 2015-06-10 株式会社神戸製鋼所 工程不良検出装置及び工程不良検出方法
JP5206697B2 (ja) * 2009-01-15 2013-06-12 新日鐵住金株式会社 連続欠陥判定方法、連続欠陥判定装置及びプログラム
KR101271795B1 (ko) * 2011-08-10 2013-06-07 주식회사 포스코 주편 하면 검사 시스템 및 검사 방법
JP2016145887A (ja) * 2015-02-06 2016-08-12 株式会社ニューフレアテクノロジー 検査装置および検査方法
EP3315950A4 (en) * 2015-06-25 2018-12-19 JFE Steel Corporation Surface flaw detection device, surface flaw detection method, and manufacturing method for steel material
JP2018005640A (ja) * 2016-07-04 2018-01-11 タカノ株式会社 分類器生成装置、画像検査装置、及び、プログラム
EP3596449A4 (en) * 2017-03-14 2021-01-06 University of Manitoba DETECTION OF STRUCTURAL DEFECTS USING AUTOMATIC LEARNING ALGORITHMS
JP6530779B2 (ja) * 2017-04-20 2019-06-12 ファナック株式会社 加工不良要因推定装置
KR102021944B1 (ko) * 2017-09-20 2019-09-17 주식회사 에이치엔에스휴먼시스템 제철소 철강제품 품질관리를 위한 지능형 결함 제어 방법 및 시스템
CN108021938A (zh) * 2017-11-29 2018-05-11 中冶南方工程技术有限公司 一种冷轧带钢表面缺陷在线检测方法以及检测系统
CN108242054A (zh) * 2018-01-09 2018-07-03 北京百度网讯科技有限公司 一种钢板缺陷检测方法、装置、设备和服务器
JP6892606B2 (ja) * 2018-03-02 2021-06-23 日本電信電話株式会社 位置特定装置、位置特定方法及びコンピュータプログラム
JP6766839B2 (ja) * 2018-03-14 2020-10-14 オムロン株式会社 検査システム、画像識別システム、識別システム、識別器生成システム、及び学習データ生成装置
JP7102941B2 (ja) * 2018-05-24 2022-07-20 株式会社ジェイテクト 情報処理方法、情報処理装置、及びプログラム
WO2020071162A1 (ja) * 2018-10-01 2020-04-09 株式会社システムスクエア 教師データ生成装置及び教師データ生成プログラム

Also Published As

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

Similar Documents

Publication Publication Date Title
MX2021007733A (es) 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.
MX2021006830A (es) Mapeo de anomalias de campo utilizando imagenes digitales y modelos de aprendizaje por maquina.
MY192327A (en) Waste composition estimation device, system, program, method, and data structure
WO2018208869A3 (en) LEARNING-BASED APPROACH TO ALIGN IMAGES ACQUIRED BY DIFFERENT TERMS
EP3913532A3 (en) Object area measurement method, apparatus, storage medium and computer product
SE1851266A1 (sv) System and method for training object classifier by machine learning
NZ757488A (en) Method of board lumber grading using deep learning techniques
WO2016140934A3 (en) Methods and apparatus for 3d image rendering
WO2014186407A3 (en) Machine learning method and apparatus for inspecting reticles
SG10201802739PA (en) Neural network systems
MX2018000340A (es) Generacion de datos de entrenamiento para deteccion de filtracion de vehiculo automatica.
ZA202102194B (en) Apparatus and method for combined visual intelligence
EP3893169A3 (en) Method, apparatus and device for generating model and storage medium
ATE463807T1 (de) Verfahren und vorrichtung zur bilderweiterung
AU2018327270A1 (en) Froth segmentation in flotation cells
MX2022013150A (es) Identificar contenedores sobrellenados.
EP3839817A3 (en) Generating and/or using training instances that include previously captured robot vision data and drivability labels
IN2015CH03905A (es)
CN105894897A (zh) 透明式书法描临校准卡
MX2021004385A (es) Dispositivo de inspeccion de billetes, metodo de inspeccion de billetes, y programa de inspeccion de billetes.
CN102567946B (zh) 一种计算机油画的生成方法和系统
Yenilmez et al. The examination of theses to relate with mathematics curriculum (2004-2013)
JP2018088223A5 (es)
Rankin et al. Learning to perceive: informing pedagogic practice through the empirical study of drawing
TW202441413A (zh) 用於產生合成缺陷影像的設備及相關的非暫時性電腦可讀媒體