Artificial Neural Networks and Their Applications in the Engineering of Fabrics

WOS: 00036372550000

Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades

2010-2011 > Academic research: refereed > Chapter in an edited book (author

Application of Neural Networks (NNs) for Fabric Defect Classification

The defect classification is as important as the defect detection in fabric inspection process. The detected defects are classified according to their types and recorded with their names during manual fabric inspection process. The material is selected as “undyed raw denim” fabric in this study. Four commonly occurring defect types, hole, warp lacking, weft lacking and soiled yarn, were classified by using artificial neural network (ANN) method. The defects were automatically classified according to their texture features. Texture feature extraction algorithm was developed to acquire the required values from the defective fabric samples. The texture features were assessed as the network input values and the defect classification is obtained as the output. The defective images were classified with an average accuracy rate of 96.3%. As the hole defect was recognized with 100% accuracy rate, the others were recognized with a rate of 95%

An Extended Review on Fabric Defects and Its Detection Techniques

In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection