Data Collection for Training Quality-Control AI in Carpet Manufacturing
Visual inspection remains the dominant quality-control practice in woven and tufted carpet production, yet it is slow, subjective, and inconsistent at the line speeds and widths of modern looms. We present a design proposal for an in-line machine-vision system whose primary purpose is twofold: to inspect the carpet web in real time and, equally importantly, to systematically collect and label images of defect patterns so that increasingly capable quality-control models can be trained over the life of the installation. The proposal is grounded in a concrete industrial setting: a Six Sigma (DMAIC) project at a woven-carpet production facility that anticipated a production bottleneck followi...
| Authors | Akbar Erkinov |
|---|---|
| Primary category | cs.CV |
| Published | 31 May 2026 |
| Last updated at source | 3 June 2026 |
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