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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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