How we helped a healthcare startup increase patient onboarding by 180%arrow_right_alt
Hard-hat detection + worker ID

Not just "someone" — flag exactly who.

A YOLOv8 model trained specifically for hard-hat compliance runs alongside a face-recognition model, so a violation isn't just a red box on a screen — it's tied to a specific person. Enroll a worker with a photo and a name, then check a second photo — or a live camera feed, continuously re-identifying and re-checking every couple of seconds — to see them matched against their PPE status.

Enrollment and matching both run entirely on-device — nothing about the photo, the name, or the computed face signature is ever sent anywhere. Use your own photo rather than a stranger's: this step only makes sense with a face whose use you actually control. ~12 MB hard-hat model self-hosted here, plus ~14 MB of face-recognition weights loaded from their publisher's CDN, first run only.
Step 1 — Enroll a worker
Step 2 — Check a site photo or live feed
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