AI USE CASE
Fabric Defect Detection via Camera
Automatically flags fabric defects on the loom for small textile mills without expensive inspection rigs.
What it is
A fixed or phone-mounted camera scans fabric rolls as they come off the loom, using computer vision to detect and bounding-box slubs, holes, stains, and miss-picks in real time. The system logs defects per metre, giving small mills a reliable seconds-vs-firsts ratio for the first time. Setup requires no specialised ML team, a plug-and-play edge device handles inference locally.
Data you need
A camera feed of fabric rolls during production, ideally with some labelled examples of known defect types (even 50–100 annotated images is sufficient to fine-tune).
Required systems
- none
Why it works
- Install consistent, diffuse LED lighting around the camera mount before any model training begins.
- Designate one quality lead who reviews the defect dashboard weekly and closes the feedback loop by labelling new edge cases.
- Start with the two or three most commercially damaging defect types rather than trying to detect everything at once.
- Use an edge inference device (e.g. NVIDIA Jetson or Raspberry Pi with Coral) to avoid cloud latency and keep running costs low.
How this goes wrong
- Lighting variations on the shop floor cause false positives or missed defects, undermining operator trust.
- No labelled defect images available at start, so the model ships with low accuracy and is abandoned before enough data accumulates.
- Defect logs are collected but never reviewed, reporting dashboard isn't adopted because no one owns the data review process.
- Camera angle or resolution is insufficient for catching fine-weave defects, leading to poor recall on the most costly fault types.
When NOT to do this
Don't deploy this if your mill has no consistent lighting and no one with 2–3 hours per week to review flagged defects and retrain the model, the system degrades quickly without a human in the loop.
Example vendors
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