An adaptive method for inspecting illumination of color intensity in transparent polyethylene terephthalate preforms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10245159" target="_blank" >RIV/61989100:27240/20:10245159 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/20:10245159
Result on the web
<a href="https://ieeexplore.ieee.org/document/9082606" target="_blank" >https://ieeexplore.ieee.org/document/9082606</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2020.2991474" target="_blank" >10.1109/ACCESS.2020.2991474</a>
Alternative languages
Result language
angličtina
Original language name
An adaptive method for inspecting illumination of color intensity in transparent polyethylene terephthalate preforms
Original language 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. (C) 2013 IEEE.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
—
Volume of the periodical
8
Issue of the periodical within the volume
2020
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
83189-83198
UT code for WoS article
000549502200123
EID of the result in the Scopus database
2-s2.0-85084955607