Purpose

Classifying the types of fabric defects in the textile industry requires a way to effectively detect. The traditional textile fabric defects detection method is human eyes, which performs very low efficiency and high cost. Therefore, how to improve the classification accuracy of textile fabric defects by using current artificial intelligence and to better meet the needs in the textile industry, the purpose of this article is to develop a method to improve the accuracy of textile fabric defects classification.

Design/methodology/approach

To improve the accuracy of textile fabric defects classification, an ensemble learning-based convolutional neural network (CNN) method in terms of textile fabric defects classification (short for ECTFDC) on an enhanced TILDA database is used. ECTFDC first adopts ensemble learning-based model to classify five types of fabric defects from TILDA. Subsequently, ECTFDC extracts features of fabric defects via an ensemble multiple convolutional neural network model and obtains parameters by using transfer learning method.

Findings

The authors applied ECTFDC on an enhanced TILDA database to improve the robustness and generalization ability of the proposed networks. Experimental results show that ECTFDC outperforms the other networks, the precision and recall rates are 97.8%, 97.68%, respectively.

Originality/value

The ensemble convolutional neural network textile fabric defect classification method in this paper can quickly and effectively classify textile fabric defect categories; it can reduce the production cost of textiles and it can alleviate the visual fatigue of inspectors working for a long time.

中文翻译：

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基于集成学习的 CNN 用于织物疵点分类

目的

对纺织行业的织物缺陷类型进行分类需要一种有效检测的方法。传统的纺织面料疵点检测方法是人眼，效率极低，成本高。因此，如何利用当前的人工智能来提高纺织面料疵点分类的准确性，更好地满足纺织行业的需求，本文的目的是开发一种提高纺织面料疵点分类准确性的方法。

设计/方法/方法

为了提高织物疵点分类的准确性，在增强型 TILDA 数据库上使用基于集成学习的卷积神经网络 (CNN) 方法对织物疵点进行分类（ECTFDC 的简称）。ECTFDC 首先采用基于集成学习的模型对 TILDA 中的五种织物缺陷进行分类。随后，ECTFDC通过集成的多卷积神经网络模型提取织物疵点特征，并通过迁移学习方法获取参数。

发现

作者将 ECTFDC 应用于增强型 TILDA 数据库，以提高所提出网络的鲁棒性和泛化能力。实验结果表明，ECTFDC 优于其他网络，准确率和召回率分别为 97.8% 和 97.68%。

原创性/价值

本文提出的集成卷积神经网络纺织品疵点分类方法可以快速有效地对纺织品疵点类别进行分类；可以降低纺织品的生产成本，缓解长时间工作的检验员的视觉疲劳。