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.

Zhoufeng Liu

Shanliang Liu

Chunlei Li

Bicao Li

International Journal of Clothing Science and Technology

涵盖服装客观测量技术、纤维和织物控制、CAD系统、产品测试、缝纫、机织和针织、检验系统、悬垂和整理等科学技术的各个方面。学术和工业研究成果发表后进行了严格审查。

This journal covers various aspects of clothing science and technology, including objective measurement techniques, fiber and fabric control, computer-aided design (CAD) systems, product testing, sewing, woven and knitted fabrics, inspection systems, draping, and finishing. All academic and industrial research findings undergo rigorous review before publication.

本期刊涵盖了服装科学与技术的各个方面，包括客观测量技术、纤维和织物控制、计算机辅助设计（CAD）系统、产品测试、缝纫、机织和针织、检验系统、悬垂和整理等。所有学术和工业研究成果在发表前均经过严格的审查。

Fabric defect detection based on multi-source feature fusion

A novel fabric defect detection method based on multi-source feature fusion which 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 sites.

Category	Quartile
工程技术	4
工程技术, 材料科学纺织	3

Fabric defect detection based on multi-source feature fusion

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Journal Info

Category	Quartile
MATERIALS SCIENCE, TEXTILES	3