Woven Fabric Density Measurement by Using Multi-Scale Convolutional Neural Networks

Abstract

Fabric density measurement plays a key role in the analysis of fabric structural parameters. Existing automatic measurement methods lack varieties of adaptability and present poor performance in practical application. In order to solve these problems, we use convolutional neural networks (CNNs) to locate warps and wefts for woven fabric density measurement. First, we use a portable wireless device to capture high-resolution fabric images and set up a new dataset with labeled yarns location. Based on this dataset, we propose an effective multi-scale convolutional neural network (MSnet) architecture to locate warps and wefts. Then, by using Hough transform and image projection of predicted yarns location, the fabric density is measured accurately. The experimental results emphasize that the proposed method has reached high accuracy under various kinds of patterns and densities of the fabrics and is superior to the state-of-the-art methods in terms of its accuracy and robustness. Promisingly, the proposed method can provide novel ideas for more fabric structural parameter analyses.