Color texture classification of yarn-dyed woven fabric based on dual-side scanning and co-occurrence matrix

Color texture classification as a part of fabric analysis is significant for textile manufacturing. In this research, a new artificial intelligence method based on a dual-side co-occurrence matrix and a back propagation neural network has been proposed for color texture classification, which could achieve relatively accurate classification results for yarn-dyed woven fabric compared with the traditional co-occurrence matrix for a single-side image. Firstly, a laboratory dual-side imaging system has been established to digitize the upper-side and lower-side images sequentially. Secondly, the dual-side co-occurrence matrix could be generated based on these dual images; four texture features could be extracted for the evaluation of the fabric texture characteristics. Thirdly, a well-trained back propagation neural network was established with the four defined features as the input vectors and the color texture type of yarn-dyed woven fabric as the output vector. The efficiency of two different classification systems based on a dual-side co-occurrence matrix and a single-side co-occurrence matrix has been compared systematically. Our experimental results show that the artificial intelligence system based on a dual-side co-occurrence matrix and back propagation neural network model could achieve a relatively better classification effect, with the high coefficient ratio ( R = 0.9726) when d = 0.