Real-time AI workplace risk detection uses computer vision to recognize unsafe conditions and behaviors as they happen and to alert someone who can act, rather than leaving the evidence in footage that is only reviewed after an incident. In forklift operations the usual targets are people walking in vehicle lanes, close passes between trucks and pedestrians, speeding in marked zones and blocked exits. The analysis can run on cameras mounted on the truck, on fixed cameras over aisles and docks, or on a site's existing camera network. Detection reduces exposure only when each alert reaches a person with the authority to correct the cause.
The full guide is Real-time safety monitoring. Two design choices decide whether a system is useful. The first is where the model runs: processing at the edge, near the camera, can trigger a local light or horn without a network round trip, while cloud processing suits trend reports more than live warnings. The second is what happens after the alert: events should flow into the same corrective-action list as near-miss reports, with an owner and a due date.
Cameras see only what is in frame and lit, so blind corners behind racking still need other controls. The hierarchy of controls ranks separation and engineering measures above warnings, and a detection layer sits beside those measures rather than replacing them.