Automated Vision System for Recognising Lycra Spandex Defects

• Autorzy: Su T. L. , Lu C. F.
• Języki publikacji: EN

Abstrakty

EN

Fabric defect detection and classifcation plays a very important role in the automatic detection process for fabrics. This paper refers to the seven commonly seen defects of lycra spandex: laddering, end-out, hole, oil spot, dye stain, snag, and crease mark. First of all, the gray level co-occurrence matrix was used to collect the features of the fabric image texture, and then the back-propagation neural network was used to establish faw classifcations of the fabric. In addition, by using the Taguchi method combined with BPNN, the BPNN drawback was improved upon, which requires overly time consuming trial-and-error to fnd the learning parameters, and could therefore converge even faster with an even smaller convergence error and better recognition rate. The experimental results proved that the fnal root-mean-square error convergence of the Taguchi-based BPNN was 0.000104, and that the recognition rate can reach 97.14%.

Słowa kluczowe

EN

lycra spandex defects; automated vision recognition; Taguchi method; neural network

• Wydawca: Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny
• Czasopismo: Fibres & Textiles in Eastern Europe
• Rocznik: 2011
• Tom: Vol. 19, no. 1 (84)
• Strony: 43--46
• Opis fizyczny: Bibliogr. 7 poz.,

Twórcy

autor

Su T. L.

autor

Lu C. F.

• Department of Cosmetic Application and Management,St. Mary's Medicine, Nursing and Management College,Yilan County 266, Taiwan, Republic of China

Bibliografia

• 1. Srinivasan K., Dastoor P. H., Radhakrishnaiah, P., Jayaraman, S.; Journal of Textile Institute, Vol. 83(3), (1992) pp.1431-1448.
• 2. Hu M.C., Tsai I.S.; Textile Research Journal, Vol. 70(8), (2000) pp. 662-670.
• 3. Tilocca A., Borzone P., Carosio S.; Textile Research Journal, Vol. 72(6), (2002) pp.545-550.
• 4. Gadelmawla E. S.; NDT & E International,Vol. 37, (2004) pp. 577-588.
• 5. Taguchi G., Elsayed E. A., Hsiang T.; Quality engineering in production systems,McGraw-Hill, New York, 1989, pp. 1-9.
• 6. Taguchi G., Konishi S., Orthogonal arrays and linear graphs: tools for quality engineering, American Supplier Institute Inc.,Michigan, 1987, p. 36.
• 7. Fowlkes W. Y., Creveling C. M.; Engineering methods for robust product design: using Taguchi methods in technology and product development, Addison-Wesley, Massachusetts, 1995, pp. 121-123.