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Fabric Defect Detection Using the Sensitive Plant Segmentation Algorithm

Research and development

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Nr DOI: 10.5604/01.3001.0013.9025

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Abstract:

Fabric quality control and defect detection are playing a crucial role in the textile industry with the development of high customer demand in the fashion market. This work presents fabric defect detection using a sensitive plant segmentation algorithm (SPSA) which, is developed with the sensitive behaviour of the sensitive plant biologically named “mimosa pudica”. This method consists of two stages: The first stage enhances the contrast of the defective fabric image and the second stage segments the fabric defects with the aid of the SPSA. The SPSA proposed was developed for defective pixel identification in non-uniform patterns of fabrics. In this paper, the SPSA was built through checking with devised conditions, correlation and error probability. Every pixel was checked with the algorithm developed to be marked either a defective or non-defective pixel. The SPSA proposed was tested on different types of fabric defect databases, showing a much improved performance over existing methods.

Tags:

external stimulation, fabric pattern, sensitive behaviour, texture.

Citation:

Fathu Nisha M, Vasuki P, Mohamed Mansoor Roomi S. Fabric Defect Detection Using the Sensitive Plant Segmentation Algorithm. FIBRES & TEXTILES in Eastern Europe 2020; 28, 3(141): 84-88. DOI: 10.5604/01.3001.0013.9025

Published in issue no 3 (141) / 2020, pages 84–88.

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FIBRES & TEXTILES
in Eastern Europe
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90-570 Łódź, Poland
e-mail: infor@lit.lukasiewicz.gov.pl
ftee@lit.lukasiewicz.gov.pl
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EDITORIAL OFFICE
FIBRES & TEXTILES in Eastern Europe 19/27 M. Skłodowskiej-Curie Str., 90-570 Łódź, Poland e-mail: infor@lit.lukasiewicz.gov.pl; ftee@lit.lukasiewicz.gov.pl

EDITORIAL DEPARTMENT
Editor-in-Chief Dariusz Wawro, Head of Editorial Office Janusz Kazimierczak, Text Editor Geoffrey Large, Assistant Editor Anna WahlProduction Łukasiewicz-ŁIT

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