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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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/ |
work_keys_str_mv |
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