Computer Vision Quality Inspection System
MedScan manufactures precision medical devices where quality failures can have life-or-death consequences. Their human inspection team caught 88% of defects, but the 12% miss rate was unacceptable — and scaling the team wasn't economically viable. They needed a vision system that could match human judgment at 10x the speed.
What wasn't working.
Manual quality inspection on the manufacturing line was slow, inconsistent, and missed 12% of defects.
What we built.
Built a multi-model computer vision pipeline with real-time defect classification and automated rejection triggers.
What it delivered.
Defect detection accuracy reached 99.2%, throughput increased 4x, and false positive rate dropped below 0.3%.
From kickoff to production.
Data Collection & Labeling
Captured 50,000+ images across 14 defect categories using high-resolution industrial cameras. Built a custom labeling pipeline with domain expert validation.
Model Architecture
Developed an ensemble of three specialized models — surface defect detector, dimensional analyzer, and anomaly classifier — with a meta-learner for final decisions.
Edge Deployment
Optimized models for real-time inference on edge GPUs, achieving <50ms per inspection with no cloud dependency.
Line Integration
Integrated with existing PLC systems and conveyor controls for automated rejection. Added a dashboard for real-time quality metrics and drift monitoring.
The stack.
“ We went from catching 88% of defects to 99.2% — and the system never gets tired or distracted. This is the future of quality assurance.
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