AI-Powered Container Defect Detection for Manufacturing QC
A packaging manufacturer was experiencing a 3.2% defect escape rate in their production line, resulting in customer returns and expensive downstream rework. Manual visual inspection was unreliable and created a quality bottleneck at 120 containers per minute.
Client
Confidential, Manufacturing Client
Team Lead
Arjun Mehta
Date
Q3 2023
Business Challenge
A packaging manufacturer was experiencing a 3.2% defect escape rate in their production line, resulting in customer returns and expensive downstream rework. Manual visual inspection was unreliable and created a quality bottleneck at 120 containers per minute.
Solution & Approach
We deployed an edge AI quality control system using YOLOv8 fine-tuned on annotated defect imagery captured from the production line. The system runs on NVIDIA Jetson edge devices positioned at the end of each production line and triggers automatic rejection at line speed.
Capabilities Delivered
- Custom YOLOv8 model training on production defect dataset
- Edge deployment on NVIDIA Jetson NX devices
- Real-time defect classification across 12 defect categories
- Automatic rejection trigger integration with PLC
- Production quality dashboard with shift-level reporting
Business Outcomes
Defect escape rate dropped from 3.2% to 0.15%, and the system processes containers at full line speed (120/min). The client recovered the implementation cost within 4 months through reduced customer returns and warranty claims.
0.15%
Defect escape rate (down from 3.2%)
4 months
Payback period
120/min
Containers inspected at line speed
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