Automated garment measurement checks every point of measure against the spec sheet in seconds, using a camera over a measuring table. Stitch and seam inspection finds skipped stitches, puckering, open seams, print and embroidery faults. Together they give apparel QC consistent numbers and images instead of tape and judgement.
Automated garment measurement uses a calibrated camera above a measuring table to find the garment outline and landmarks, then calculates each point of measure and compares it with the spec sheet tolerance. An operator lays the garment flat, the system measures it, and the result is saved against style, size and order.
Stitch and seam inspection uses close-up imaging to check seams for defects that matter to buyers: skipped or broken stitches, uneven stitch density, puckering, open seams and seam slippage, as well as print, embroidery and stain faults. Stitch and seam types are defined in ISO 4915 and ISO 4916, and in ASTM D6193.
Both support, rather than replace, the AQL inspection that buyers specify, typically based on ISO 2859-1 sampling. They make each inspected garment faster to check, the results consistent between inspectors, and the evidence available when a buyer questions a shipment.
The operator places the garment on a backlit or contrasting measuring table and selects the style and size, or scans the bundle ticket.
A calibrated overhead camera captures the whole garment; close-up cameras or a handheld device capture seams when stitch inspection is needed.
Software finds the outline and landmarks and calculates each point of measure in millimetres.
Each measurement is compared with the spec sheet tolerance, and seam images are checked for defects.
Pass or fail per point, defect images and a full report are saved per garment, size and order.
A camera calibrated against a reference grid converts pixels into millimetres across the whole measuring table, correcting for lens distortion.
Deep learning finds the landmarks used in each point of measure, such as shoulder points, armholes, hem and waistband corners, for each garment type.
Measurements are compared with the tech pack tolerances for the style and size, with results by point of measure.
High-resolution close-ups of seams reveal stitch spacing, skipped stitches, puckering and open seams.
Models trained on good and defective examples find print, embroidery, stain and fabric faults on the garment surface.
Two example project scopes showing how the station, the checks and the outputs are defined. Every plant is different, so the final configuration is confirmed after a feasibility study on your own parts.
The situation: A knitwear factory measures garments by tape at final inspection, and buyers question measurements on stretch fabrics.
The situation: A bottoms factory receives buyer complaints about open seams and skipped stitches found after shipment.
Industrial cameras, lenses, line-scan cameras and code readers are available through our machine vision catalog, quoted with delivery to Sri Lanka. We select the hardware for your application during the feasibility study.
Browse the hardware catalogEvery product, surface and line behaves differently under a camera, so every project starts with evidence from your own material.
A site visit to understand your product, line speed, quality criteria and where the check should sit.
We image your good and defective samples and show what the system can reliably detect. This is the go or no-go.
One station on one line, running beside your inspectors until results match and your team trusts them.
More lines, integration with your PLC and systems, and local support from our Sri Lankan engineering team.
Camera-based fabric inspection for woven and knit rolls. Line-scan cameras detect holes, stains, slubs and dropped stitches across the full width, map every defect and grade rolls to the 4-point system.
Automatic counting of parts, garments, cartons and packs by camera, sensor or weight, with counts sent to your PLC or ERP. Designed and built in Sri Lanka.
AI on your CCTV detects missing helmets, vests, gloves and hairnets, danger zone entry, forklift near misses and smoke. Processed on site, PDPA ready.
Sort fabric rolls, cut panels and washed garments into buyer shade bands with CIELAB measurement and 555 codes.
Every machine vision application we design and build, in one place.
Accuracy depends on calibration, camera resolution, table size and how consistently the garment is laid flat. The camera removes tape-holding differences, but laying the garment is still a manual step, so we set up the table and procedure for your styles and agree the measurement tolerance with you.
Yes, and it often helps most there, because a camera does not pull the fabric as a tape can. The garment still needs to be laid flat without stretching, which the table and procedure are designed to support.
Yes. Points of measure and tolerances are taken from your tech packs or spec sheets for each style and size, so results are reported in the same terms your buyers use.
Yes, with close-up imaging of the seams. It finds skipped and broken stitches, uneven stitch density, puckering and open seams. Stitch and seam types follow ISO 4915, ISO 4916 and ASTM D6193.
No. Buyers specify AQL sampling, typically to ISO 2859-1. Automated measurement and inspection make each inspected garment faster and more consistent, and give images and data to support the result.
Tell us what you need to check and share some good and faulty samples. We will show you what a camera can reliably see before you commit to anything.
Photographs show representative plants and equipment, not Cerox installations or client sites. Diagrams by Cerox Engineering. Photo credits, via Wikimedia Commons: Garment factory sewing floor by YeraC, CC BY-SA 4.0; Industrial sewing machine by Haddybellz, CC BY-SA 4.0.