An Automated Fabric Fault Detection and Classification System Based on Computer Vision and Soft Computing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24410%2F13%3A%230003701" target="_blank" >RIV/46747885:24410/13:#0003701 - isvavai.cz</a>

Výsledek na webu

<a href="http://acc-ern.tul.cz/images/journal/sbornik/ACC-Journal_1-2013.pdf" target="_blank" >http://acc-ern.tul.cz/images/journal/sbornik/ACC-Journal_1-2013.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

An Automated Fabric Fault Detection and Classification System Based on Computer Vision and Soft Computing

Popis výsledku v původním jazyce

Fabric inspection is one of the essential quality control processes in weaving mills. The automation of this process using computer vision systems is expected to increase the efficiency of the process and increase the total profit revenues on the long run. This work introduces a computer vision system that has the capability to detect and classify a relatively large number of fabric defects. Image enhancement techniques were used in processing the fabric acquired images. Spatial and spectral features were extracted from the processed images and used as inputs to soft-computing classifiers. Two approaches were used in the classification with the aim of reducing the calculation time required during the image analysis. The successful classification rate was 97.3% using the direct approach that has a slightly longer processing time. The performance of the classifiers in the series approach ranges between 91 to 100% depending on the classification level and the used image features. Results

Název v anglickém jazyce

An Automated Fabric Fault Detection and Classification System Based on Computer Vision and Soft Computing

Popis výsledku anglicky

Fabric inspection is one of the essential quality control processes in weaving mills. The automation of this process using computer vision systems is expected to increase the efficiency of the process and increase the total profit revenues on the long run. This work introduces a computer vision system that has the capability to detect and classify a relatively large number of fabric defects. Image enhancement techniques were used in processing the fabric acquired images. Spatial and spectral features were extracted from the processed images and used as inputs to soft-computing classifiers. Two approaches were used in the classification with the aim of reducing the calculation time required during the image analysis. The successful classification rate was 97.3% using the direct approach that has a slightly longer processing time. The performance of the classifiers in the series approach ranges between 91 to 100% depending on the classification level and the used image features. Results

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JS - Řízení spolehlivosti a kvality, zkušebnictví

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ACC Journal

ISSN

1803-9782

e-ISSN

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Svazek periodika

19

Číslo periodika v rámci svazku

A

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

16-24

Kód UT WoS článku

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EID výsledku v databázi Scopus

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