Key takeaways
- H2O Hydrogen Torch trains image classification, object detection and segmentation models on your own labeled inspection images, without writing deep learning code.
- A defect classifier can sort each image into the defect classes your quality team already uses, such as scratches, crazing, pitted surface or rolled-in scale.
- Inspection prioritization ranks lots, batches or units by predicted defect risk from process and test data, built with H2O Driverless AI.
- Models run in deployment options that keep images and process data inside the plant.
Send inspectors to the parts that matter
Manual visual inspection is slow and tiring, and the same defect can be called differently by two inspectors on two shifts. Sampling helps with volume, but a sample can miss the lot that matters.
Rule-based vision systems help on a stable line. They struggle when the product mix changes, when lighting or surface finish varies, or when a new defect type appears that no rule describes, so you end up trading line speed against the risk of an escape.