An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms

Machine vision systems are applied in industry to control the quality of production while optimizing efficiency. A machine vision and AI-based inspection of color intensity in transparent Polyethylene Terephthalate (PET) preforms is especially sensitive to backgrounds and lighting, therefore, much a...

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Main Authors: Darius Drungilas, Mindaugas Kurmis, Zydrunas Lukosius, Sergej Jakovlev, Miroslav Voznak
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9082606/
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spelling doaj-6fa0a79d04ea426cadd2b6c5de4a83f52021-03-30T01:44:07ZengIEEEIEEE Access2169-35362020-01-018831898319810.1109/ACCESS.2020.29914749082606An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate PreformsDarius Drungilas0Mindaugas Kurmis1Zydrunas Lukosius2Sergej Jakovlev3https://orcid.org/0000-0002-1440-8221Miroslav Voznak4Marine Research Institute, Klaipeda University, Klaipeda, LithuaniaMarine Research Institute, Klaipeda University, Klaipeda, LithuaniaMarine Research Institute, Klaipeda University, Klaipeda, LithuaniaMarine Research Institute, Klaipeda University, Klaipeda, LithuaniaIT4Innovations National Supercomputing Center, VSB-Technical University of Ostrava, Ostrava, Czech RepublicMachine vision systems are applied in industry to control the quality of production while optimizing efficiency. A machine vision and AI-based inspection of color intensity in transparent Polyethylene Terephthalate (PET) preforms is especially sensitive to backgrounds and lighting, therefore, much attention is given to its illumination conditions. The paper examines the adverse factors affecting the quality of image recognition and presents an adaptive method for reducing the influence of changing illumination conditions in the color inspection process of transparent PET preforms. The method is based on predicting measured color intensity correction parameters according to illumination conditions. To test this adaptive method, a hardware and software system for image capture and processing was developed. This system is capable of inspecting large quantities of preforms in real time using a neural network with a modified gradient descent and momentum algorithm. The experiment showed that correction of the measured color intensity value reduced the standard deviation caused by variable and uneven illumination by 61.51%, demonstrating that machine vision color intensity evaluation is a robust and adaptive solution under illuminated conditions for detecting abnormalities in machine-based PET inspection procedures.https://ieeexplore.ieee.org/document/9082606/Image processingmachine visionneural netsdata mining
collection DOAJ
language English
format Article
sources DOAJ
author Darius Drungilas
Mindaugas Kurmis
Zydrunas Lukosius
Sergej Jakovlev
Miroslav Voznak
spellingShingle Darius Drungilas
Mindaugas Kurmis
Zydrunas Lukosius
Sergej Jakovlev
Miroslav Voznak
An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms
IEEE Access
Image processing
machine vision
neural nets
data mining
author_facet Darius Drungilas
Mindaugas Kurmis
Zydrunas Lukosius
Sergej Jakovlev
Miroslav Voznak
author_sort Darius Drungilas
title An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms
title_short An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms
title_full An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms
title_fullStr An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms
title_full_unstemmed An Adaptive Method for Inspecting Illumination of Color Intensity in Transparent Polyethylene Terephthalate Preforms
title_sort adaptive method for inspecting illumination of color intensity in transparent polyethylene terephthalate preforms
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description Machine vision systems are applied in industry to control the quality of production while optimizing efficiency. A machine vision and AI-based inspection of color intensity in transparent Polyethylene Terephthalate (PET) preforms is especially sensitive to backgrounds and lighting, therefore, much attention is given to its illumination conditions. The paper examines the adverse factors affecting the quality of image recognition and presents an adaptive method for reducing the influence of changing illumination conditions in the color inspection process of transparent PET preforms. The method is based on predicting measured color intensity correction parameters according to illumination conditions. To test this adaptive method, a hardware and software system for image capture and processing was developed. This system is capable of inspecting large quantities of preforms in real time using a neural network with a modified gradient descent and momentum algorithm. The experiment showed that correction of the measured color intensity value reduced the standard deviation caused by variable and uneven illumination by 61.51%, demonstrating that machine vision color intensity evaluation is a robust and adaptive solution under illuminated conditions for detecting abnormalities in machine-based PET inspection procedures.
topic Image processing
machine vision
neural nets
data mining
url https://ieeexplore.ieee.org/document/9082606/
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