TY  - JOUR
TI  - The effect of wavelet transform for fabric defect classification
AU  - ÇIKLAÇANDIR FATMA GÜNSELİ
AU  - UTKU SEMİH
AU  - ÖZDEMİR HAKAN
T2  - Industria Textila
AB  - An automatic control system during fabric production improves production quality. For this reason, the number of
automatic systems developed is increasing day by day. These systems use different methods, different data sets, and
different approaches. We investigate the effects of wavelet transform using four different feature sets (wavelet-based
Principal Component Analysis, wavelet-based Gray Level Co-occurrence Matrix, Principal Component Analysis and
Gray Level Co-occurrence Matrix). The methods of K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are
used as classifiers. Experiments have been carried out for six different fabric defects (fly, dirty warp, tight-loose warp,
fibrous weft, rub mark and weft stack) on 57 images. The experimental results performed show that wavelet-based PCA
(Principal Component Analysis) and KNN have been optimal methods for achieving the highest success rate. We have
achieved a 92.9825% accuracy rate by using them.
DA  - 2022/04/30/
PY  - 2022
DO  - 10.35530/IT.073.02.202030
VL  - 73
IS  - 02
SP  - 165
EP  - 170
UR  - http://revistaindustriatextila.ro/images/2022/2/008%20FATMA%20Revista%20IndustriaTextila%20No2_2022.pdf
ER  -