Purpose

The paper aims to build a new objective evaluation method of fabric pilling by combining an integrated image analysis technology with a deep learning algorithm.

Design/methodology/approach

Series of image analysis techniques were adopted. First, a Fourier transform transformed images into the frequency domain. The optimal resolution matrix of an exponential high-pass filter was determined by combining the energy algorithm. Second, the multidimensional discrete wavelet transform determined the optimal division level. Third, the iterative threshold method was used to enhance images to obtain a complete and clear pilling ball images. Finally, the deep learning algorithm was adopted to train data from pilling ball images, and the pilling levels were classified according to the learning features.

Findings

The paper provides a new insight about how to objectively evaluate fabric pilling grades. Results of the experiment indicate that the proposed objective evaluation method can obtain clear and complete pilling information and the classification accuracy rate of the deep learning algorithm is 94.2%, whose structures are rectified linear unit (ReLU) activation function, four hidden layers, cross-entropy learning rules and the regularization method.

Research limitations/implications

Because the methodology of the paper is based on woven fabric, the research study’s results may lack generalizability. Therefore, researchers are encouraged to test other kinds of fabric further, such as knitted and unwoven fabrics.

Originality/value

Combined with a series of image analysis technology, the integrated method can effectively extract clear and complete pilling information from pilled fabrics. Pilling grades can be classified by the deep learning algorithm with learning pilling information.

中文翻译：

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基于图像分析和深度学习算法的织物起球客观评价

目的

本文旨在通过将集成图像分析技术与深度学习算法相结合，构建一种新的织物起球客观评价方法。

设计/方法/方法

采用了一系列的图像分析技术。首先，傅里叶变换将图像转换到频域中。结合能量算法确定指数高通滤波器的最佳分辨率矩阵。其次，多维离散小波变换确定了最佳划分级别。第三，采用迭代阈值法对图像进行增强，得到完整清晰的起球球图像。最后采用深度学习算法对起球球图像数据进行训练，并根据学习特征对起球级别进行分类。

发现

该论文提供了关于如何客观评估织物起球等级的新见解。实验结果表明，所提出的客观评价方法可以获得清晰完整的起球信息，深度学习算法的分类准确率为94.2%，其结构为整流线性单元（ReLU）激活函数、四个隐藏层、交叉熵学习规则和正则化方法。

研究限制/影响

由于论文的方法论是基于机织织物，研究结果可能缺乏普遍性。因此，鼓励研究人员进一步测试其他种类的织物，例如针织和无纺布。

原创性/价值

结合一系列图像分析技术，该集成方法可以有效地从起毛织物中提取清晰完整的起球信息。起球等级可以通过深度学习算法与学习起球信息进行分类。