Manufacturing AI Vision Inspection
Project Lead · Data Scientist & AI Engineer
Problem
Defect classification quality and consistency were limited by class imbalance and noisy production-floor image data.
Approach
- Designed an end-to-end vision pipeline from data collection to model deployment.
- Applied ResNet-based training with weighted sampling and domain-specific augmentation.
- Integrated edge-to-server inference with gRPC for low-latency production usage.
Impact
- Achieved 98%+ defect classification accuracy.
- Improved fine-grained defect typing reliability in real operations.
- Published M.S. thesis based on production-grade outcomes.