Quality control vision inspection

Requirements describe capabilities the pattern needs in your environment, not the vendors you must buy. Any system that fills a requirement satisfies it — that’s what makes the catalog portable across the long tail of SMB tooling.

1. image_capture_source

   REQUIREDREADstream

   Where images of items come from. Physical setup matters: lighting, angle, consistency.

   DATA SHAPE

   Image or short video clip with item identifier, capture timestamp, line/station identifier.
   COMMONLY FILLED BY

   • camera installed over a production line
   • imaging station integrated with the conveyor system
   • handheld scanner used by inspectors
   • phone-based capture at pack-out stations
2. defect_taxonomy_and_examples

   REQUIREDREADcorpus

   What 'bad' looks like, specifically. The pattern learns from examples; without enough labeled examples per defect class, it can't be trusted.

   DATA SHAPE

   Per-defect-class: name, description, decision rule, set of labeled positive and negative example images.
   COMMONLY FILLED BY

   • labeled dataset built during the engagement
   • ongoing labeling workflow where QC inspectors add to the dataset
   • structured defect catalog the operations team maintains
3. item_record_lookup

   REQUIREDREADrequest

   What item is being inspected and what its specifications are. Different items have different inspection criteria.

   DATA SHAPE

   Per-item SKU with inspection specification, packaging requirements, customer-specific variations.
   COMMONLY FILLED BY

   • ERP item master with QC specifications
   • product catalog with inspection criteria
   • customer-specific shipment requirements
4. inspection_decision_destination

   REQUIREDWRITEevent

   Where pass/fail decisions go. Pass-through for accepted items, divert for rejected items.

   DATA SHAPE

   Decision per item: accept/reject/uncertain, defect class if rejected, confidence score, image stored for review.
   COMMONLY FILLED BY

   • control signal to the production line diverter
   • scan log in the warehouse management system
   • result feed into the operations dashboard
5. human_qc_review_station

   REQUIREDREAD + WRITErequest

   Where uncertain or borderline cases get human verification. Critical for the cases the pattern doesn't handle confidently.

   DATA SHAPE

   Image with proposed classification, confidence score, comparison to similar cases. Reviewer marks final decision.
   COMMONLY FILLED BY

   • dedicated review station at the QC desk
   • tablet at the inspection point with the review UI
   • remote review queue for off-line review
6. audit_image_archive

   REQUIREDWRITEcorpus

   Stored images of inspected items for traceability and quality investigations. Particularly important in regulated industries.

   DATA SHAPE

   Image with item identifier, inspection result, timestamp, retention metadata.
   COMMONLY FILLED BY

   • object store with retention policies
   • QC archive in the manufacturing operations system
   • compliance archive for regulated products
7. model_retraining_loop

   REQUIREDREAD + WRITEbatch

   How human verdicts feed back to improve the pattern. Without this loop, the model can't adapt to new defect types or environmental changes.

   DATA SHAPE

   Reviewed cases with original prediction, human verdict, image, reason for override.
   COMMONLY FILLED BY

   • weekly batch of reviewed cases added to the training set
   • continuous labeling pipeline where each correction feeds back
   • quarterly model retraining process