Computer Vision Quality & Safety
Inspection that never blinks.
Computer vision systems that inspect every product, monitor every camera and flag defects or safety risks in real time. Consistent across shifts and deployable on the edge.
01 · Overview
Sampling misses what matters
Manual inspection is slow and inconsistent, and it degrades over a shift. Sampling checks only a fraction of output, and safety monitoring depends on someone watching a screen.
Built with
- Detection & segmentation
- Edge inference
- Active learning
Deployment
On-premise, private cloud or in-region.
02 · How it works
Trained on your defects
- 1
Capture the right images
We advise on cameras, lighting and placement, or work with your existing CCTV and line cameras.
- 2
Train on your defects
Detection and segmentation models fine-tuned on your products, with anomaly-based methods where defects are rare.
- 3
Run at line speed
Models optimised for edge devices or GPU servers so inspection keeps pace with production.
- 4
Close the loop
Defects logged with images for root-cause analysis; operator confirmations retrain the model.
- Line & CCTV camerasExisting or new
- Edge inference deviceRuns at line speed
- Detection modelsDefects · PPE · zones
- Reject signal & alertsPLC, dashboard, SMS
- Operator reviewLabels ↺ retraining
03 · Capabilities
What it inspects
- Surface defects, paint and finish inspection
- Assembly verification and missing components
- Packaging, labelling and contamination checks
- PPE compliance and restricted-zone safety
- Crack and damage detection for infrastructure
04 · Outcomes
What we measure
- Fewer escaped defects
- Defect escape rate / customer returns
- Full coverage
- Share of output inspected
- Safer sites
- Safety events detected; time to response
- Faster root cause
- Time from defect spike to identified cause
Example scenarios
Illustrative- Automotive linePaint defect and assembly verification.
- Food productionPackaging and contamination inspection.
- WarehousePPE and forklift-zone safety monitoring.
- Infrastructure operatorCrack detection from drone and vehicle imagery.
05 · Governance
Deployment & privacy
Controls built in
- Edge devices or on-premise GPU servers
- Images stay on site
- Operator review interface
- Faces blurred automatically in safety footage
Technology
- Python
- PyTorch
- OpenCV
- YOLO
- Detectron2
- TensorRT
- ONNX Runtime
- NVIDIA Jetson
06 · FAQ
Common questions
How many defect images do we need?
Fewer than you might think. Anomaly-based methods can start from mostly good samples, and active learning targets the images that matter.
Can we use our existing cameras?
Often yes, especially for safety monitoring. For fine surface defects we may recommend dedicated cameras and lighting.
Will it slow down the production line?
No. Models are optimised to run within your cycle time on edge hardware next to the line.
Inspect everything, not a sample.
Send us sample images and we will show you what the model sees.
Start a project
