Fabric defect detection based on multi-source feature fusion

• Published in: International journal of clothing science and technology Vol. 34; no. 2; pp. 156 - 177
• Main Authors: Liu, Zhoufeng, Liu, Shanliang, Li, Chunlei, Li, Bicao
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 11.03.2022; Emerald Group Publishing Limited
• Subjects: Accuracy; Adaptability; Algorithms; Datasets; Deep learning; Defects; Design defects; Fractals; Machine learning; Morphology; Neural networks; Object recognition; Technical services; Training; Wavelet transforms; Multiple source feature fusion; Deep neural networks; Transfer learning; Object detection
• ISSN: 0955-6222; 1758-5953
• DOI: 10.1108/IJCST-07-2020-0108

PurposeThis 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/approachTo 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.FindingsThe 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/implicationsOur proposed algorithm can provide a promising tool for fabric defect detection.Practical implicationsThe 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 implicationsThis 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 industrialOriginality/valueTherefore, our proposed algorithm can provide a promising tool for fabric defect detection.