Fabric defect detection based on multi-source feature fusion. Issue 2 (21st June 2021)

• Record Type: Journal Article
• Authors: Liu, Zhoufeng; Liu, Shanliang; Li, Chunlei; Li, Bicao
• Is Part Of: International journal of clothing science and technology. Volume 34:Issue 2(2022)
• Page Start: 156
• Page End: 177
• Publication Date: 2021-06-21
• Subjects: Object detection -- Transfer learning -- Deep neural networks -- Multiple source feature fusion; Clothing and dress -- Periodicals; Textile fabrics -- Periodicals; 677
• DOI: 10.1108/IJCST-07-2020-0108
• Languages: English
• ISSNs: 0955-6222

Abstract

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.