[직/편성물] Fabric Stitching Inspection Using Segmented Window Technique and BP Neural Network 출판일 : 2009.01.01 저자 : C.W.M. Yuen, W.K. Wong, S.Q. Qian, D.D. Fan, L.K. Chan, and E.H.K. Fung 서지사항 : Textile Reseach Journal, Volume 79, Issue 1, 24페이지 등록일 : 2011.08.10 I 조회수 : 798 작성자 : admin |
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%.
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