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8 March 1999 Real-time vision system for defect detection and neural classification of web textile fabric
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Abstract
A real-time pilot system for defect detection and classification of web textile fabric is presented in this paper. The general hardware and software platform, developed for solving this problem, is presented and a powerful novel method for defect detection is proposed. This method gives good results in the detection of low contrast defects under real industrial conditions, where the presence of many types of noise is an inevitable phenomenon. For the defect classification an artificial neural network, trained by using a back-propagation algorithm, is implemented. Using a reduced number of possible defect classes, the system gives consistent and repeatable results with sufficient speed.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Panagiotis Mitropoulos, Christos Koulamas, Radovan D. Stojanovic, Stavros Koubias, George D. Papadopoulos, and George Karayanis "Real-time vision system for defect detection and neural classification of web textile fabric", Proc. SPIE 3652, Machine Vision Applications in Industrial Inspection VII, (8 March 1999); https://doi.org/10.1117/12.341126
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