Computer Vision QC
Challenge
The Problem
Manual fabric inspection employed 12 QC workers per shift to visually check for defects. Defect escape rate was 6.2% — meaning 1 in 16 defective meters left the factory. Inspection speed bottlenecked the line at 28 m/min.
Approach
Our Solution
We installed 4 industrial cameras above the production line with stroboscopic lighting. A computer vision model trained on 80,000 labelled fabric samples detects defects in real time at 60 m/min. Defect locations are marked, and the line can be auto-stopped for critical defects. The system was calibrated per fabric type.
Outcomes
Results Achieved
↓ 85% defect escape rate (6.2% → 0.9%)
Line speed increased from 28 to 60 m/min
QC headcount redeployed to higher-value roles (no redundancies)
Defect classification accuracy 99.2% across 14 defect types
Business Impact
ROI Statement
Defect-related rework and customer returns reduced by ~$310,000 annually. Line throughput increase adds ~$180,000. Total: ~$490,000 annual benefit on a $195,000 project.
Your turn
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