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Article
Publication date: 14 December 2021

Zhoufeng Liu, Menghan Wang, Chunlei Li, Shumin Ding and Bicao Li

The purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and…

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Abstract

Purpose

The purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and improve quality control in textile manufacturing.

Design/methodology/approach

This paper proposed a dual-branch balance saliency model based on discriminative feature for fabric defect detection. A saliency branch is firstly designed to address the problems of scale variation and contextual information integration, which is realized through the cooperation of a multi-scale discriminative feature extraction module (MDFEM) and a bidirectional stage-wise integration module (BSIM). These modules are respectively adopted to extract multi-scale discriminative context information and enrich the contextual information of features at each stage. In addition, another branch is proposed to balance the network, in which a bootstrap refinement module (BRM) is trained to guide the restoration of feature details.

Findings

To evaluate the performance of the proposed network, we conduct extensive experiments, and the experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) approaches on seven evaluation metrics. We also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed method.

Originality/value

The dual-branch balance saliency model was proposed and applied into the fabric defect detection. The qualitative and quantitative experimental results show the effectiveness of the detection method. Therefore, the proposed method can be used for accurate fabric defect detection and even surface defect detection of other industrial products.

Details

International Journal of Clothing Science and Technology, vol. 34 no. 3
Type: Research Article
ISSN: 0955-6222

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Article
Publication date: 21 June 2021

Zhoufeng Liu, Shanliang Liu, Chunlei Li and Bicao Li

This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep…

282

Abstract

Purpose

This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep learning-based, which incurs some additional problems: (1) The model is difficult to train due to too few fabric datasets for the difficulty of collecting pictures; (2) The detection accuracy of existing methods is insufficient to implement in the industrial field. This study intends to propose a new method which can be applied to fabric defect detection in the industrial field.

Design/methodology/approach

To cope with exist fabric defect detection problems, the article proposes a novel fabric defect detection method based on multi-source feature fusion. In the training process, both layer features and source model information are fused to enhance robustness and accuracy. Additionally, a novel training model called multi-source feature fusion (MSFF) is proposed to tackle the limited samples and demand to obtain fleet and precise quantification automatically.

Findings

The paper provides a novel fabric defect detection method, experimental results demonstrate that the proposed method achieves an AP of 93.9 and 98.8% when applied to the TILDA(a public dataset) and ZYFD datasets (a real-shot dataset), respectively, and outperforms 5.9% than fine-tuned SSD (single shot multi-box detector).

Research limitations/implications

Our proposed algorithm can provide a promising tool for fabric defect detection.

Practical implications

The paper includes implications for the development of a powerful brand image, the development of “brand ambassadors” and for managing the balance between stability and change.

Social implications

This work provides technical support for real-time detection on industrial sites, advances the process of intelligent manual detection of fabric defects and provides a technical reference for object detection on other industrial

Originality/value

Therefore, our proposed algorithm can provide a promising tool for fabric defect detection.

Details

International Journal of Clothing Science and Technology, vol. 34 no. 2
Type: Research Article
ISSN: 0955-6222

Keywords

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Article
Publication date: 20 January 2021

Xueqing Zhao, Min Zhang and Junjun Zhang

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…

627

Abstract

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.

Details

International Journal of Clothing Science and Technology, vol. 33 no. 4
Type: Research Article
ISSN: 0955-6222

Keywords

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