The improved method in fabric image classification using convolutional neural network

Wool fabric is an important material in the weaving sector despite its disadvantages, such as the difficulty of classifying and storing pattern information and identifying when this information needs to be reused. With the deep learning method, these problems can be ingeniously solved. Therefore, the use of convolutional neural networks (CNNs) in artificial intelligence to extract texture features is a helpful and vital approach to resolving fabric pattern classification problems in fabric traceability and management. In this paper, we combined the unique advantages of Inception and ResNet to improve the feature extractor. After training the new CNN with fabric images, the texture features are well extracted and the fabric categories are properly classified through optimized classifiers. Different data are employed to confirm the generalization and robustness of the model, including training with different image sizes and small training sets. The proposed network model outperforms the classic deep learning classification algorithms in both accuracy and speed.