[직/편성물] Detecting Fabric Defects with a Neural Network Using Two Kinds of Optical Patterns 출판일 : 2002.06.01 저자 : Antonio Tilocca, Paolo Borzone, Stefano Carosio, Antonio Durante 서지사항 : Textile Research Journal, Volume 72, No 6(2002), 545-550 페이지 등록일 : 2012.10.08 I 조회수 : 986 작성자 : admin |
In this work, we present a new direct approach to automatic fabric inspection based on an
optical acquisition system and an artificial neural network (ANN) to analyze the acquired
data. Defect detection and classification are based both on gray levels and 3D range profile
data of the sample. These patterns are simultaneously fed into a feed-forward neural
network without further transformation. The ANN is trained to classify three different
categories: normal fabric, defect with a marked 3D component, and defect with no 3D
component. The good classification rate obtained shows that the double set of patterns
acquired basically includes relevant information on the textile sample. Since no further
transformation of the data is needed before classification, the response of this system can
be very fast and thus suitable for on-line monitoring of fabric defects at a high inspection
rate.
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