Fabric material identification based on Densenet variant networks

Abstract

The identification of textile fiber materials is a tedious task. Traditional physical or chemical methods rely on specialist knowledge and expensive instruments. Currently, the deep learning-based methods are limited by the lack of sufficient image samples. In this paper, we propose a method for the classification of textile materials based on advanced densely connected convolutional networks (Densenet) using fabric surface images. Firstly, we selected a small sample dataset containing five types of materials and performed some pre-processing, including colour weakening and data enhancement. The lightweight network Densenet was then used as the main body to ensure the low parameters of the network, making it more suitable for the recognition of small sample image datasets. The network is then fused with the attention mechanism, Orthogonal Softmax Layer (OSL) and the depth-separable convolution mechanism, and we call the final network AONet. It has only 0.64 M (million) parameters. In this paper, we tested four metrics of AONet for five types of fabric images, and the results showed that the recognition accuracy was 95.61%. The precision, recall and F1 scores were 94.87%, 94.31% and 94.40%, respectively.