I work on the manufacturing side of electronics and we’ve been exploring whether GPU-accelerated inference is worth integrating into our AOI (automated optical inspection) pipeline for PCB defect detection.
On paper it makes sense faster image processing, better model accuracy with CNNs trained on defect datasets, real-time classification. In practice I’m running into questions I can’t find clean answers to:
- At what production volume does the GPU inference setup actually pay off versus a well-tuned traditional AOI system?
- Has anyone trained models on small defect datasets (we’re talking a few thousand labeled images, not millions) and gotten reliable results in production or does the dataset size make this impractical?
- Any experience with Jetson-based edge deployment for this kind of inspection workload?
I’m not a pure ML engineer I come from the hardware/manufacturing side so if anyone’s bridged this gap in a real production environment rather than a research setting, I’d genuinely like to hear what actually worked and what didn’t.