Technical Overview
Vision inspection that runs at your line speed.
Three-layer architecture: camera input → per-frame inference → pass/reject signal to PLC. Local inference in under 8ms. No cloud dependency for real-time decisions.
Architecture
Camera to PLC in under 8ms — no cloud in the loop.
Inference runs on-premises on the edge node. The reject gate fires before the unit reaches end-of-line. Cloud receives aggregate metrics and model updates only — your production floor doesn't depend on a network connection.
Up to 4 streams
USB3 Vision supported
RTSP streams accepted
8-core, 16GB RAM min
NVIDIA MX550+ GPU
<8ms inference latency
Runs entirely offline
Standard food-line relay
OPC-UA integration
Modbus TCP supported
No cloud dependency for real-time decisions. Cloud used for model updates and reporting dashboard only.
Defect Detection
Measurable thresholds, not "anomaly detection"
Each defect class is specified with a numeric threshold, trained on your product, and tunable per SKU through the dashboard. This matters for HACCP documentation — your CCP rejection criteria have to be defined and recorded, not opaque.
- Bruising greater than 5mm² surface area
- Discoloration when ΔE exceeds 3 vs reference
- Mold spotting and surface bloom detection
- Mechanical damage and impact marks
- Skin splits and rupture detection on produce
- Label skew beyond ±0.5mm tolerance
- Missing or partial date codes and batch codes
- Print smear, fade, and registration errors
- Barcode readability pre-verification
- Label lift, bubble, and adhesion failures
- Underfill and overfill detection in flexible packs
- Package deformation and crush detection
- Cap height and closure completeness
- Net weight proxy via dimensional volumetrics
- Seal zone contamination — debris, product ingress
- Seal wrinkle and partial weld patterns
- Hard plastic and packaging fragment detection
- Bone chip and dense-visible foreign objects
Model Training
Trained on your line. Deployed on your hardware.
A model trained on generic food imagery won't generalize to your SKUs, your lighting, or your defect patterns. We collect data from your production environment and train specifically for what your line makes. Your model stays on your edge node — it doesn't leave your facility.
We collect 200–500 images per defect class directly from your production environment. Typically done in one to two production shifts using a portable camera rig mounted above the line.
Training is scoped to your product variants and SKUs — not generic food images. Threshold sensitivity tunable per defect class and per SKU. Typically takes 3–5 days from data collection to first deployment.
Model deployed to your on-premises edge node. Threshold levels adjustable through the dashboard without retraining. Quarterly model refreshes included on Production and Facility plans.
Performance Envelope
Results by line speed
From pilot deployments. Actual performance varies by product type, defect class complexity, and line configuration.
| Line Speed | Detection Rate | False Reject Rate | Recommended Resolution |
|---|---|---|---|
| 200 u/min | ~97% | <0.15% | 1.3 MP GigE, 50fps |
| 400 u/min | ~96% | <0.20% | 2 MP GigE, 100fps |
| 600 u/min | ~94% | <0.30% | 4 MP GigE, 120fps |
| 800 u/min | ~91% | <0.40% | 5 MP GigE, 150fps |
Results from pilot deployments. Detection rate is weighted average across defect classes; individual class rates vary. False reject rate measured after initial threshold calibration on your product. Results vary by product geometry, defect class, and lighting conditions. We share these numbers because hiding them would waste your time — your line may perform better or require adjustment.
Ready to spec it for your line?
Tell us your line speed, product type, and the defect class causing the most retailer chargebacks. We'll come back with a specific camera configuration and expected detection envelope — not a generic sales deck.