How we deploy real-time computer vision models on Raspberry Pi and Jetson Nano for industrial quality control applications.
Edge AI is the Future
Running ML models in the cloud introduces latency that's unacceptable for real-time applications like quality control on production lines. Edge deployment brings inference to where the data is generated.
Model Optimization Pipeline
Our workflow for deploying YOLO models to edge devices:
- Train on GPU server with full-precision weights
- Export to ONNX format for portability
- Quantize to INT8 using TensorRT (NVIDIA) or ONNX Runtime
- Benchmark on target hardware
- Deploy with our inference wrapper
Performance Results
On a Jetson Nano with TensorRT optimization:
- YOLOv8n: 30 FPS at 640x640
- YOLOv8s: 15 FPS at 640x640
- Custom defect detector: 25 FPS at 416x416
Real-World Impact
One of our manufacturing clients reduced defect escape rate by 94% after deploying our vision system. The system processes 120 items per minute with 98.5% accuracy.
Our Emathrix Vision Toolkit includes 5 pre-trained models ready for edge deployment.