Defects Identification in Textile by Means of Artificial Neural Networks

  • Authors:

  • Vitoantonio Bevilacqua;Lucia Cariello;Giuseppe Mastronardi;Vito Palmieri;Marco Giannini

  • Affiliations:

  • Department of Electrical and Electronics, Polytechnic of Bari, Bari, Italy 70125 and e.B.I.S. s.r.l. (electronic Business in Security), Spin-Off of Polytechnic of Bari, Valenzano (BA), Italy 70010;Department of Electrical and Electronics, Polytechnic of Bari, Bari, Italy 70125 and e.B.I.S. s.r.l. (electronic Business in Security), Spin-Off of Polytechnic of Bari, Valenzano (BA), Italy 70010;Department of Electrical and Electronics, Polytechnic of Bari, Bari, Italy 70125 and e.B.I.S. s.r.l. (electronic Business in Security), Spin-Off of Polytechnic of Bari, Valenzano (BA), Italy 70010;Department of Electrical and Electronics, Polytechnic of Bari, Bari, Italy 70125;Centro Laser s.c.a.r.l., , Valenzano (BA), Italy 70010

  • Venue:
  • ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
  • Year:
  • 2008

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Abstract

In this paper we use a neural network approach for defects identification in textile. The images analyzed came from an artificial vision system that we used to acquire and memorize them in bitmap file format. The vision system is made of two grey scale line scan camera arrays and each array is composed of four CCD cameras with a sensor of 2048 pixels. Every single camera has a field of view of 600mm. The big amount of pixels to be studied to determine whether the texture is defective or not, requires the implementation of some encoding technique to reduce the number of the significant elements. The artificial neural networks (ANN) are manipulated to compress a bitmap that may contain several defects in order to represent it with a number of coefficients that is smaller than the total number of pixel but still enough to identify all kinds of defects classified. An error back propagation algorithm is also used to train the neural network. The proposed technique includes, also, steps to break down large images into smaller windows or array and eliminate redundant information.