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TSFabrics: A Time-Series Fabric Dataset for Real-Time Defect Detection on Circular Knitting Machines.

Yan-Qin Ni1, Pei-Kai Huang2,3, Wei-Jen Wang1

  • 1Department of Computer Science and Information Engineering, National Central University, Taoyuan City, Taiwan.

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|February 7, 2026
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Summary
This summary is machine-generated.

A new Time-Series fabric dataset (TSfabrics) captures real-world industrial variations for improved fabric defect detection. This dataset aids in developing models for continuous production lines.

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Area of Science:

  • Materials Science
  • Computer Vision
  • Industrial Engineering

Background:

  • Existing fabric datasets lack real-world industrial complexity, using isolated, non-time-series images.
  • This limits the development of effective fabric defect detection systems for continuous manufacturing.

Purpose of the Study:

  • Introduce the Time-Series fabric dataset (TSfabrics) for realistic fabric defect detection.
  • Provide a high-quality dataset reflecting continuous fabric manufacturing processes.

Main Methods:

  • Collected 93,196 grayscale images across 22 continuous production scenarios on circular knitting machines.
  • Varied illumination, production speeds, and fabric types to simulate diverse industrial conditions.
  • Included pixel-level annotations for defect-free and defective samples, distinguishing true defects from 'cutlines'.

Main Results:

  • TSfabrics captures the continuous nature of fabric manufacturing, unlike previous datasets.
  • The dataset encompasses a wide range of fabric defect types.
  • Annotations accurately differentiate defects from non-defect features like 'cutlines'.

Conclusions:

  • TSfabrics is a valuable resource for advancing practical fabric defect detection.
  • Enables the development and evaluation of models suitable for continuous production lines.
  • Bridges the gap between academic research and industrial applications in fabric quality control.