False positive rate of less than 1%

A false positive rate under 1%at 100% detection rate.

Built to stay low, automatically

The false alarm rate does not degrade over time thanks to four core features:

Automatic calibration: Adapts to lighting changes, camera wear, and part drift without retraining or operator intervention.
Image alignment: Corrects part positioning and robot axis variations automatically.
Activation clustering: Tolerately labeled non-critical variations (like minor burrs) stop triggering alerts.
Per-region thresholds: Sets precise limits on defect size ($\text{mm}^2$) and total surface percentage (%).

It is self-healing: auto-calibration monitors the baseline continuously and readjusts, notifying you only if a major physical shift occurs.

A low false alarm rate means nothing if you miss defects. What matters is achieving near-zero false alarms while keeping detection at 100%.

False alarms cost you twice: good parts scrapped or re-inspected, and operators who stop trusting the system. Vision systems do not die from missed defects; they die from false positives, turned off by teams that no longer believe them.

Where conventional AI systems run at 20% to 53% false alarms, DeepHawk delivers 0.0% to 2.4% across published use casesstaying under 1% on most lines. That is a 10x improvement.

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