ArticleScientific data2026
TSFabrics: A Time-Series Fabric Dataset for Real-Time Defect Detection on Circular Knitting Machines.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- TSFabrics: A Time-Series Fabric Dataset for Real-Time Defect Detection on Circular Knitting Machines.Scientific data · 2026Article
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Authors and funding
5 authors.
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Abstract
Existing fabric datasets consist of isolated and non-time-series images, failing to reflect the complexities and variations present in real-world industrial environments. To support fabric defect detection in real-world industrial environments, we present a high-quality Time-Series fabric dataset (TSfabrics). By capturing time-series fabric images, TSfabrics reflects the continuous and complex nature of actual fabric manufacturing on circular knitting machines. TSfabrics encompasses 22 continuous fabric production scenarios and consists of 93196 grayscale fabric images collected under diverse production conditions, including different illuminations, production speeds, and fabric types. In addition, TSfabrics contains defect-free and defective samples, with pixel-level annotations that differentiate true defects from production-related "cutlines" found in defect-free fabric images. Meanwhile, TSfabrics covers a wide range of fabric defect types. To sum up, TSfabrics offers a valuable resource for advancing practical fabric defect detection methods and bridging the gap between academic research and industrial application. We believe that TSfabrics enables the development and evaluation of defect detection models that are better suited for deployment on continuous production lines.
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Registered trials
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