MANUFACTURING
Package & Label Inspection
Package & Label Inspection. What it takes to run it.
Ensuring accurate labeling, sealing, and barcode readability.
// 01 · The question
“Is the right label on the right pack, straight, sealed and readable?”
// 02 · Why it is hard
Line speed. The decision has to be made in the time between packs, on motion-blurred frames, and a false reject stops the line just as expensively as a false accept lets a bad pack through.
// 03 · What the labels have to be
Bounding box
Decided before the first frame
Every check here is a region and a rule: is the label inside its window, is the seal continuous along its line, does the barcode decode. Boxes localise the regions and the rules do the rest.
This is the decision that is expensive to reverse. The geometry has to match what the answer contains, and finding out it does not means labelling the set a second time.
// 04 · How we run it
Three parts of one loop, on this job.
LexAnnotate
Defect Annotation
Pixel-level defect annotation for surface scratches, cracks, dents, weld faults, and assembly misalignments. Multi-sensor alignment for multi-camera inspection systems.
LexInsight
Line Performance
Connect defect detections to production data - batch numbers, machine IDs, shift schedules. Identify defect patterns across lines and predict maintenance needs before unplanned downtime.
LexAlert
Quality Alerts
Monitor defect detection accuracy across changing production conditions. Detect when lighting shifts, new materials, or equipment aging degrades model performance. Auto-retrain to maintain 99%+ accuracy.
// 05 · What breaks it after launch
Packaging gets redesigned on a marketing schedule nobody tells the model about. The new artwork is correct and the model has never seen it, so a good pack starts failing on the day of the changeover.
It is not the only one that can get this use case, it is the one that usually gets it first. All ten conditions.
// 06 · In manufacturing
Defect Detection at Scale
The Challenge
Vision models performed well in pilots but lost accuracy as factory conditions shifted.
Our Execution
Built pixel-level annotated datasets and multi-sensor alignment for reliable defect detection.
The Result
Maintained 99%+ defect detection accuracy across changing production environments. Reduced unplanned downtime.
99%+
Accuracy maintained in production