Application Of Fuzzy Wavelet Algorithm In Feature Extraction And Defect Identification Of Textile

The textile industry is driven by the need for quality control and monitoring in all phases of production. One very important step in quality assurance is the defect detection and identification on the surface of textile. However, there is no effective feature detection system available and the inspection is conducted manually. This is both ineffective and expensive. To all these questions, this paper designs a whole intelligent architecture which has practical value in feature extraction and defect identification. First it uses the knowledge in image processing to preprocess all the images of defect. Then their features become distinct by using the technique of edge detection. In the feature extraction and identification, the paper brings forward the fuzzy and wavelet algorithm which combines the fuzzy theory and wavelet transform technique. The features are extracted by using wavelet transform and then these features are fuzzied. At last the inferencing machine identifies the sort of defect according to the knowledge in the knowledge-base. At the same time some performance metrics are provided. According to these performance metrics, the fuzziness process can accommodate to exterior disturbance and improve classification ability. Lots of experiments and stimulations indicate this algorithm is feasible in theory and the identification and classification is satisfying.