Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29642
Title: Fabric stitching inspection using segmented window technique and BP neural network
Authors: Yuen, CWM
Wong, WK 
Qian, SQ
Fan, DD
Chan, LK
Fung, EHK
Keywords: Fabric defects
Fabric inspection
Fabric stitching
Image segmentation
Neural network
Segmented window technique
Issue Date: 2009
Publisher: SAGE Publications
Source: Textile research journal, 2009, v. 79, no. 1, p. 24-35 How to cite?
Journal: Textile research journal 
Abstract: In the textile and clothing industry, much research has been conducted on fabric defect automatic detection. However, few have been specifically designed for evaluating fabric stitches or seams of semi-finished and finished garments. In this paper, a fabric stitching inspection method is proposed for knitted fabric in which a segmented window technique is developed to segment images into three classes using a monochrome single-loop ribwork of knitted fabric: (1) seams without sewing defects; (2) seams with pleated defects; and (3) seams with puckering defects caused by stitching faults. Nine characteristic variables were obtained from the segmented images and input into a Back Propagation (BP) neural network for classification and object recognition. The classification results demonstrate that the inspection method developed is effective in identifying the three classes of knitted-fabric stitching. It is proved that the classifier with nine characteristic variables outperformed those with five and seven variables and the neural network technique using either BP or radial basis (RB) is effective for classifying the fabric stitching defects. By using the BP neural network, the recognition rate was 100%.
URI: http://hdl.handle.net/10397/29642
ISSN: 0040-5175
EISSN: 1746-7748
DOI: 10.1177/0040517508090503
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

14
Last Week
0
Last month
0
Citations as of Aug 10, 2017

WEB OF SCIENCETM
Citations

14
Last Week
0
Last month
0
Citations as of Aug 13, 2017

Page view(s)

36
Last Week
2
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.