Wool knitted fabric pilling objective evaluation based on double-branch convolutional neural network

Abstract

In the objective evaluation of wool knitted fabric pilling, the feature extraction step is a key factor affecting performance. In this paper, we proposed a double-branch deep cross-level fusion convolutional neural network (D-DCFNet) to improve feature selection. First, a cross-level fusion module (CLF module), based on a Fire module in SqueezeNet, was created to improve the feature extraction capability of a single module. Then, we designed a double-branch structure D-DCFNet. One branch consists of a CLF module, the core feature extraction module, and the other branch consists of a Fire module. Next, the features extracted from the two branches were fused together. Finally, the model trained by D-DCFNet was used to classify the knitting pilling data set to evaluate the robustness of the model. Experiments showed that D-DCFNet’s rating accuracy for woolen knitted fabrics and semi-worsted knitted fabrics is 99.35% and 99.02%, respectively, when the model size is only 5.77 M.

Keywords:

• Wool knitted fabric
• pilling
• CNN
• double-branch
• cross-level fusion module

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.