"AI-powered" PPE detection, read literally
Camera systems verify one thing, that a piece of equipment is visible in frame. That's the bottom of the hierarchy of controls, dressed up as safety intelligence.
Computer-vision systems that flag a missing hard hat or safety glasses are an easy sell: the camera sees a violation, logs it, and the dashboard fills with compliance data. Read literally, though, the system verifies exactly one thing, whether a piece of equipment is visible in frame. It infers nothing about whether that equipment is worn correctly, rated for the hazard, or even relevant to the task in front of the worker.
Where PPE actually sits
That distinction matters because PPE is the last line of defence, not the first. The NIOSH hierarchy of controls, echoed in ANSI/ASSP Z10, ranks controls by reliability: elimination and substitution at the top, then engineering controls, then administrative controls, and PPE at the very bottom, because it depends entirely on human behaviour and does nothing about the hazard itself.
A tool that maximises PPE-detection counts optimises the weakest tier while leaving the stronger ones untouched. It can make a site look safer, more green checks, fewer flagged frames, while the elimination and engineering work that would actually remove the hazard never gets budgeted, because the dashboard says compliance is at 98%.
The literal-reading test
For each thing the system claims to "detect," finish this sentence: this proves that ___. "A hard hat is visible" is not "the head hazard is controlled." When you write out the honest proof statements, you'll see exactly how much of your safety case is resting on the lowest, least reliable tier of control.
None of this makes PPE detection useless, verifying the last line of defence has a place. It makes it narrow. Sold as the top of your safety stack, it quietly inverts the hierarchy: the most-watched control is the one that matters least.