An adaptive method for inspecting illumination of color intensity in transparent polyethylene terephthalate preforms
Identifikátory výsledku
Kód výsledku v 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>
Nalezeny alternativní kódy
RIV/61989100:27740/20:10245159
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
An adaptive method for inspecting illumination of color intensity in transparent polyethylene terephthalate preforms
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
An adaptive method for inspecting illumination of color intensity in transparent polyethylene terephthalate preforms
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20201 - Electrical and electronic engineering
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2020
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
IEEE Access
ISSN
2169-3536
e-ISSN
—
Svazek periodika
8
Číslo periodika v rámci svazku
2020
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
10
Strana od-do
83189-83198
Kód UT WoS článku
000549502200123
EID výsledku v databázi Scopus
2-s2.0-85084955607