Computer Vision Beyond the Demo

Replaceable sample post: swap this file with your own article when the blog is ready.

Object detection demos are easy to make look convincing. Reliable vision systems require a broader view of the pipeline: input quality, latency, tracking behavior, failure modes, and the operating environment.

Begin with the camera and the constraint

Resolution, lighting, motion blur, frame rate, and camera placement often matter more than a small change in model architecture. Write those constraints down before comparing models.

Treat false positives as product behavior

A detector that raises an alert too often can be as unusable as one that misses an object. Review errors by scenario, not only by aggregate accuracy, and decide which errors are acceptable for the workflow.

Keep inference observable

Record model version, input dimensions, confidence thresholds, and latency percentiles. Those details make it possible to explain a production regression instead of guessing at it.