Manual fabric inspection depends on a person watching cloth move past, shift after shift. At loom speeds, small defects slip through and are often found only when a buyer rejects a roll. AI fabric defect detection replaces occasional checking with continuous checking.
What the system sees
Cameras mounted along the loom line capture the cloth continuously. Steady lighting matters as much as the camera itself, because shadows and glare can look like defects. A good setup starts with a check of camera coverage and lighting on your actual line.
How the model decides
A computer vision model is trained on labelled images of real defects such as holes, slubs, oil stains, broken ends and missing picks. For every frame it looks for regions that do not match normal cloth and classifies what it finds. The model gets better when it is trained on images from your own fabrics, not only generic ones.
From detection to action
Detection only pays off if something happens next. A practical system alerts the supervisor straight away, can stop the loom, and logs each defect by type, loom and shift so that quality reports for buyers are generated rather than written by hand.
What a mill needs to start
- A look at your line to confirm camera positions and lighting
- Sample images of the defects that cost you the most
- A short list of target defects and fabrics to begin with
- A pilot with success metrics agreed in advance
Questions to ask any vendor
- Does it keep up with the speed of your loom?
- How many false alarms should you expect, and how are they handled?
- Has it been trained on fabrics like yours?
- What reports do your buyers actually receive?
NeuAI’s FabricGuard is designed for loom-line speeds of 120+ metres per minute and works with existing cameras. You can read more on the fabric defect detection page, or start with the free AI readiness audit.
