The Manufacturing Quality Challenge
Quality control has traditionally relied on human inspectors: slow, inconsistent, and expensive. Our client was spending $2.4M annually on manual inspection with a defect escape rate of 3.5%.
System Architecture
Hardware Setup
- Cameras: 4 high-resolution industrial cameras (5MP) with GigE Vision interface
- Lighting: Structured LED lighting to highlight surface defects
- Edge Computing: NVIDIA Jetson Xavier NX for real-time inference at the production line
Software Stack
| Component | Technology |
|---|---|
| Image Capture | GigE Vision SDK |
| Detection Model | YOLOv8 Custom-trained |
| Inference Server | NVIDIA Triton |
| Dashboard | React + WebSocket real-time |
| Data Pipeline | Apache Kafka + Spark |
Model Training Pipeline
We collected 50,000+ labeled images of defects over 3 months. The YOLOv8 model was fine-tuned on our custom dataset with aggressive data augmentation.
from ultralytics import YOLO
model = YOLO("yolov8x.pt")
results = model.train(
data="defect_dataset.yaml",
epochs=100,
imgsz=640,
batch=16,
augment=True
)
model.export(format="engine", half=True)Results
- Defect Detection Accuracy: 99.2% (up from 96.5% human)
- Inspection Speed: 200ms per part (vs. 15 seconds manual)
- Cost Reduction: 60% annual savings
- ROI Achieved: 8 months
