Artificial Neural Network to Construct a Pattern Recognition System for Inspecting Fabric Fefects

P. Satyanarayan; M. V. Ramana Murthy; Srikanth Nivathi; B. Chithra

– In this paper, we evaluate the efficiency and accuracy of a method of detecting fabric defects that have been classified into different categories by a neural network. Four kinds of fabric defects most likely to be found during weaving were learned by the network. Based on the principle of the back-propagation algorithm of learning rule, fabric defects could be detected and classified exactly. The method used for processing image feature extraction is a co-occurrence-based method, by which six feature parameters are obtained. All of them consist of contrast measurements, which involve three spatial is placements (i.e., 1, 12, 16) and four directions (0, 45, 90, 135 degrees) of fabric defects’ images used for classification. The results show that fabric defects inspected by means of image recognition in accordance with the artificial neural network agree approximately with initial expectations. It has become more and more important for textile engineers to use automatic techniques in production processes and management procedures. At present, fabric inspection still depends on human sight, and inspection results are greatly influenced by the mental and physical condition of an inspector. To economize on personnel and to increase the competitive ability of products it is necessary to automate the inspection of fabric defects during weaving. In this study, we use a neural network topology known as “multi-layer perception,” which has not been used in the past because of the lack of effective training algorithms for it. Recently this has changed due to the development of an iterative gradient procedure known as the back-propagation algorithm [7]. Through this supervised learning algorithm, the neural network can become a classifier of fabric defect Because of the highly parallel operation and quick response nature of a neural network, it is easy to apply this method to on-line monitoring of fabric defects in weaving.

Select Volume / Issues:

• Volume 7,Issue 2, Mar. 2016

Year:

2016

Type of Publication:

Article

Keywords:

Neural Networks; Knitted Fabrics; Sigmoid Transfer Function; Textile; Pattern Recognition

Journal:

IJECCE

Volume:

7

Number:

2

Pages:

116-121

Month:

March

ISSN:

2249-071X