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.

摘要

在毛针织物起球的客观评价中，特征提取步骤是影响性能的关键因素。在本文中，我们提出了一种双分支深度跨级融合卷积神经网络（D-DCFNet）来改进特征选择。首先，基于 SqueezeNet 中的 Fire 模块创建了跨级融合模块（CLF 模块），以提高单个模块的特征提取能力。然后，我们设计了一个双分支结构的 D-DCFNet。一个分支包含一个 CLF 模块，即核心特征提取模块，另一个分支包含一个 Fire 模块。接下来，将从两个分支中提取的特征融合在一起。最后，利用D-DCFNet训练的模型对针织起球数据集进行分类，评估模型的鲁棒性。