A hybrid automatic defect detection method for Thai woven fabrics using CNNs combined with an ANN

Wannida Sae-Tang; Atthaphon Ariyarit

doi:10.1117/12.2626141

30 April 2022 A hybrid automatic defect detection method for Thai woven fabrics using CNNs combined with an ANN

Wannida Sae-Tang, Atthaphon Ariyarit

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121770O (2022) https://doi.org/10.1117/12.2626141
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

Thai silk is a main export product of Thailand. Since it is a luxury and high-cost product, its quality must be controlled and guaranteed. Automatic defect detection of Thai fabrics especially for Thai silk then becomes an interesting research issue. This paper proposes a hybrid automatic defect detection method for Thai woven fabrics using convolutional neural networks (CNNs) combined with an artificial neural network (ANN). Original images and local homogeneity images are used for CNN training, and gray level co-occurrence matrix (GLCM) texture statistics are used for ANN training. The results from two CNNs and an ANN are then combined by voting. The experimental results show that the proposed hybrid method is superior to the conventional methods in terms of accuracy.

Citation Download Citation

Wannida Sae-Tang and Atthaphon Ariyarit "A hybrid automatic defect detection method for Thai woven fabrics using CNNs combined with an ANN", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121770O (30 April 2022); https://doi.org/10.1117/12.2626141

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