[직/편성물] A Back-Propagation Neural Network for Recognizing Fabric Defects 출판일 : 2003.02.01 저자 : Chung-Feng Jeffrey Kuo, Ching-Jeng Lee 서지사항 : Textile Research Journal, Volume 73, No 2(2003), 147-151 페이지 등록일 : 2012.06.27 I 조회수 : 756 작성자 : admin |
Appearance is an important property of fabrics. Traditionally, fabric ins on is done by
workers, but it is so subjective that accuracy is a problem because inspectors tire easily
and suffer eyestrain. To overcome these disadvantages, an image system is used as the
detecting tool in this paper. A plain white fabric is adopted as the sample, and the
distinguishing defects are holes, oil stains, warp-lacking, and weft-lacking. An area scan
camera with 512 × 512 resolution is used in the scheme, and a grabbed image is
transmitted to a computer for filtering and thresholding. The corresponding image data are
then used in the back-propagation neural network as input. There are three input units,
maximum length, maximum width, and gray level of fabric defects, in the input layer of the
neural network. This system is successfully employed to determine nonlinear properties and
enhance recognition.
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