An Automated System for Fabric Faults Inspection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24410%2F17%3A00004278" target="_blank" >RIV/46747885:24410/17:00004278 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.amazon.com/Automated-System-Fabric-Faults-Inspection/dp/3330842628" target="_blank" >http://www.amazon.com/Automated-System-Fabric-Faults-Inspection/dp/3330842628</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

An Automated System for Fabric Faults Inspection

Popis výsledku v původním jazyce

This work utilizes a digital camera to acquire and transmit fabric images to a computer which enhances and extracts the features for each image.Then, the features are processed using Artificial Intelligence technique to detect and classify if the fabric has a defect or not and classify 10 fabric defects. Two approaches have been used for classification using statistical features only, spectral features only or both. The first approach classifies all the defects in one step. The results show that using both statistical and spectral features with each other give a 95.5% correct classification. The second approach classifies the defect on three steps.The first step classifies if the fabric sample has a defect or free defect. The results show that statistical features get the best classification with the least time with 91% percentage. The second step classifies the direction of the defect; Area, Warp or weft. The use of both features results, a 95.5% classification rate. The third step classifies the defect. For the area defects, Fourier features get a 100% classification. While using statistical features results a 100% correct classification for warp and weft defects.

Název v anglickém jazyce

An Automated System for Fabric Faults Inspection

Popis výsledku anglicky

This work utilizes a digital camera to acquire and transmit fabric images to a computer which enhances and extracts the features for each image.Then, the features are processed using Artificial Intelligence technique to detect and classify if the fabric has a defect or not and classify 10 fabric defects. Two approaches have been used for classification using statistical features only, spectral features only or both. The first approach classifies all the defects in one step. The results show that using both statistical and spectral features with each other give a 95.5% correct classification. The second approach classifies the defect on three steps.The first step classifies if the fabric sample has a defect or free defect. The results show that statistical features get the best classification with the least time with 91% percentage. The second step classifies the direction of the defect; Area, Warp or weft. The use of both features results, a 95.5% classification rate. The third step classifies the defect. For the area defects, Fourier features get a 100% classification. While using statistical features results a 100% correct classification for warp and weft defects.

Druh

B - Odborná kniha

CEP obor

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OECD FORD obor

20503 - Textiles; including synthetic dyes, colours, fibres (nanoscale materials to be 2.10; biomaterials to be 2.9)

Projekt

<a href="/cs/project/LO1201" target="_blank" >LO1201: ROZVOJ ÚSTAVU PRO NANOMATERIÁLY, POKROČILÉ TECHNOLOGIE A INOVACE TECHNICKÉ UNIVERZITY V LIBERCI</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

ISBN

9783330842625

Počet stran knihy

164

Název nakladatele

Noor Publishing

Místo vydání

Germany

Kód UT WoS knihy

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