Open AccessDefect detection in textile fabrics with optimal Gabor filter and BRDPSO algorithm
Author(s) -
Jiawei Zhang,
Yueyang Li,
Huiwu Luo
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1651/1/012073
Subject(s) - gabor filter , particle swarm optimization , feature selection , algorithm , computer science , feature (linguistics) , filter (signal processing) , artificial intelligence , pattern recognition (psychology) , swarm behaviour , textile , classifier (uml) , gabor wavelet , mathematics , feature extraction , computer vision , materials science , composite material , discrete wavelet transform , linguistics , philosophy , wavelet transform , wavelet
This paper presents an effective method that can detect fabric defects. The method utilizes the optimal Gabor filter and binary random drift particle swarm algorithm (BRDPSO) that can implement feature selection and parameter optimization synchronously. The parameters of 2D-Gabor filters are adjusted by quantum-behaved particle swarm optimization algorithm (QPSO) and the optimal Gabor filter is obtained. BRDPSO is used to select features on the original feature set and simultaneously optimize the parameters of the Isolation Forest (IF) classifier. Extensive experimental results indicate that the proposed method has effective detecting performance on the defect detection of textile fabric.