Computer vision with YOLO: from model to production workflow

YOLO is a very useful model family for detecting objects in images or video streams. But a powerful demonstrator does not automatically become a reliable tool on the ground.

Quality starts with real conditions

Cameras, angle of view, light, speed, occlusions, network quality: these elements have a direct impact on the result. Before choosing the model, you must understand what needs to be detected, under what conditions and with what acceptable level of error.

The model must be inserted into a process

A detection is of value only if it triggers a useful action: alert, count, control, data enrichment or human decision. The interface, event storage, thresholds and supervision are part of the final product.

Measure before deploying

Global accuracy is not enough. We need to analyze false positives and false negatives on important cases, track drifts and predict a loop for data improvement. This is the condition for moving from an impressive AI to an usable AI.