VerdefSoftTECHNOLOGIES
Solution / AI Visual Inspection

AI Visual Inspection

Evaluate detection, segmentation and anomaly models against the defects and uncertainty that matter to your process.

The industrial problem

Defects can vary in shape, texture or frequency. A high aggregate accuracy score may conceal missed critical defects or excessive false rejection.

The solution approach

Define the defect taxonomy and labeling rules. Compare a conventional baseline with classification, detection, segmentation or anomaly-detection approaches. Separate training and evaluation data by production source or batch where appropriate.

Reference architecture

01 →Image acquisition
02 →Dataset + labeling rules
03 →Training + model evaluation
04 →Versioned edge inference
05 →Accept / reject / review
06 / OUTPUTImage + result traceability

Illustrative sequence. Interfaces and timing are specified for the actual equipment and process.

Typical applications

  • Surface defect detection and segmentation
  • Object detection and classification
  • Anomaly detection for unusual appearance
  • AI-assisted inspection with operator review
  • Inspection evidence linked to a part or batch

Expected business value

A way to address variable visual defects and prioritize review. False accept, false reject, latency and coverage are acceptance criteria, not assumed outcomes.

What needs validation

  • Keep a held-out dataset that represents realistic variation.
  • Report per-defect results and test unseen conditions.
  • Define a review state for uncertain, missing or out-of-scope images.
Model performance depends on data coverage and image quality. No commercial deployment, defect-detection rate or production readiness is claimed by these capability descriptions.
Start with the engineering problem

What does your process
need to do better?

Share the part, the machine, the constraint and the result you need. That is the starting point for a useful technical conversation.

Talk to an Engineer