Computer Vision in Public Transit: Lessons from Hayden AI

Deploying computer vision in public transit is not a machine learning problem. It's a systems integration problem with a machine learning component. The model is maybe 20% of the work. The other 80% is environmental hardening, edge case handling, legal compliance, and fleet operations.

At Hayden AI, our cameras needed to operate in rain, snow, direct sunlight, night conditions, and the constant vibration of a city bus. The model accuracy in the lab was 97%. In the field, it dropped to 89% in the first week. The gap was entirely environmental — reflections, occlusions, camera shake.

The fix was not better models. It was better preprocessing. We added frame stabilization, adaptive exposure correction, and a confidence threshold that varied by time of day and weather conditions. The model stayed the same. The system around it got smarter.

The legal dimension surprised me the most. Every jurisdiction has different rules about automated enforcement evidence. Some require two independent frames showing the violation. Some require visible license plates. Some require human review of every citation before issuance. The AI system had to be architected around legal constraints, not just technical ones.

The biggest lesson: AI deployment at fleet scale is a logistics problem. You need over-the-air model updates that don't interrupt service. You need monitoring dashboards that fleet operators can actually use. You need failure modes that degrade gracefully — a camera that can't classify should still record, not crash.

These lessons directly shaped how I built ATLAS Vision. Every design decision — the streaming architecture, the confidence scoring, the multi-model pipeline — comes from watching what breaks when AI meets the real world at scale.

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I work with enterprises on AI infrastructure, defense technology, and operational intelligence.