CL2022001166A1 - Aplicación dirigida de aprendizaje profundo a un equipo de inspección visual automatizada - Google Patents

Aplicación dirigida de aprendizaje profundo a un equipo de inspección visual automatizada

Info

Publication number
CL2022001166A1
CL2022001166A1 CL2022001166A CL2022001166A CL2022001166A1 CL 2022001166 A1 CL2022001166 A1 CL 2022001166A1 CL 2022001166 A CL2022001166 A CL 2022001166A CL 2022001166 A CL2022001166 A CL 2022001166A CL 2022001166 A1 CL2022001166 A1 CL 2022001166A1
Authority
CL
Chile
Prior art keywords
container
visual inspection
images
automated visual
deep learning
Prior art date
Application number
CL2022001166A
Other languages
English (en)
Inventor
Neelima Chavali
Thomas C Pearson
Manuel A Soto
Jorge Delgado Torres
Rentas Roberto C Alvarado
Javier O Tapia
Sandra Rodriguez-Toledo
Eric R Flores-Acosta
Osvaldo Perez
Brenda A Torres
Original Assignee
Amgen Inc
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 Amgen Inc filed Critical Amgen Inc
Publication of CL2022001166A1 publication Critical patent/CL2022001166A1/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
    • 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
    • G01N21/8803—Visual inspection
    • 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
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • 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

Landscapes

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

Abstract

En un método para potenciar la precisión y la eficiencia en la inspección visual automatizada de recipientes, un recipiente que contiene una muestra se orienta de tal modo que una cámara de exploración de líneas tiene una vista de perfil de un borde de un tapón del recipiente. Una pluralidad de imágenes del borde del tapón es capturada por la primera cámara de exploración de líneas mientras se gira el recipiente, donde cada imagen de la pluralidad de imágenes corresponde a una posición de rotación diferente del recipiente. Se genera una imagen bidimensional del borde del tapón basándose al menos en la pluralidad de imágenes, y píxeles de la imagen bidimensional son procesados, por uno o más procesadores que ejecutan un modelo de inferencia que incluye una red neuronal entrenada, para generar datos de salida indicativos de una probabilidad de que la muestra sea defectuosa.
CL2022001166A 2019-11-07 2022-05-04 Aplicación dirigida de aprendizaje profundo a un equipo de inspección visual automatizada CL2022001166A1 (es)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201962932413P 2019-11-07 2019-11-07
US201962949667P 2019-12-18 2019-12-18

Publications (1)

Publication Number Publication Date
CL2022001166A1 true CL2022001166A1 (es) 2023-02-10

Family

ID=73654910

Family Applications (1)

Application Number Title Priority Date Filing Date
CL2022001166A CL2022001166A1 (es) 2019-11-07 2022-05-04 Aplicación dirigida de aprendizaje profundo a un equipo de inspección visual automatizada

Country Status (12)

Country Link
US (1) US20220398715A1 (es)
EP (1) EP4055559A1 (es)
JP (1) JP2022553572A (es)
KR (1) KR20220090513A (es)
CN (1) CN114631125A (es)
AU (1) AU2020378062A1 (es)
BR (1) BR112022008676A2 (es)
CA (1) CA3153701A1 (es)
CL (1) CL2022001166A1 (es)
IL (1) IL291773A (es)
MX (1) MX2022005355A (es)
WO (1) WO2021092297A1 (es)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230153978A1 (en) * 2021-11-17 2023-05-18 Communications Test Design, Inc. Methods and systems for grading devices
US20230184738A1 (en) * 2021-12-15 2023-06-15 Optum, Inc. Detecting lab specimen viability

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5309486A (en) * 1992-11-12 1994-05-03 Westinghouse Electric Corp. Non-contact flaw detection for cylindrical nuclear fuel pellets
JP4547828B2 (ja) * 2001-04-19 2010-09-22 大日本印刷株式会社 容器搬送システム
EP1560017B1 (en) * 2002-10-18 2009-08-05 Kirin Techno-System Company, Limited Glass bottle inspection device
US20090154789A1 (en) * 2007-12-17 2009-06-18 Gradience Imaging, Inc. System and method for detecting optical defects
TWI708052B (zh) * 2011-08-29 2020-10-21 美商安美基公司 用於非破壞性檢測-流體中未溶解粒子之方法及裝置
US10899138B2 (en) * 2017-01-11 2021-01-26 Applied Vision Corporation Container inspection system controlling printheads to correct for detected ink thickness errors
JP2019002725A (ja) * 2017-06-13 2019-01-10 コニカミノルタ株式会社 欠陥検査装置
EP3673258A1 (en) * 2017-08-25 2020-07-01 Baxter International, Inc. Automated visual inspection for visible particulate matter in empty flexible containers

Also Published As

Publication number Publication date
BR112022008676A2 (pt) 2022-07-19
CN114631125A (zh) 2022-06-14
AU2020378062A1 (en) 2022-04-07
JP2022553572A (ja) 2022-12-23
MX2022005355A (es) 2022-06-02
EP4055559A1 (en) 2022-09-14
US20220398715A1 (en) 2022-12-15
WO2021092297A1 (en) 2021-05-14
IL291773A (en) 2022-06-01
KR20220090513A (ko) 2022-06-29
CA3153701A1 (en) 2021-05-14

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