AI & Automation
EYEFIRE — Factory AI Safety System
Discipline
Computer Vision / AI
Real-time PPE detection, behavioral safety monitoring, and fire detection on the factory floor.

The brief
What it is and how it was built.
A factory has hazards everywhere, and safety staff cannot watch every corner at once. EYEFIRE watches the cameras the factory already has, all day and all night.
It reads the RTSP feeds from the existing CCTV, so no new cameras are needed. YOLOv8 models check in real time whether workers are wearing helmets, safety vests, gloves and eye protection. When someone in a marked zone is missing their gear, the floor supervisor gets an alert on the dashboard and on their phone.
A second layer looks at behaviour that PPE checks miss: people walking into restricted machine zones, standing too close to moving equipment, or lifting in unsafe postures. A separate model detects fire and smoke, with less than one second between ignition and alert.
EYEFIRE runs on NVIDIA Jetson devices installed next to the cameras. Nothing has to be uploaded to the cloud, so alerts arrive faster and the video never leaves the plant, which matters to clients with strict data rules. A React dashboard gives safety managers a live view of the whole plant, the incident history, compliance trends and figures for each shift.
Scope delivered
What we delivered.
- 01Real-time PPE checks: helmet, vest, gloves, eye protection
- 02Behaviour checks, such as people entering restricted machine zones
- 03Fire and smoke alerts in under one second
- 04Runs on NVIDIA Jetson inside the plant, with no cloud delay
- 05Live dashboard with incident log and reports
Category
AI & Automation
Technology
YOLOv8 · Python · OpenCV · TensorFlow · NVIDIA Jetson · RTSP Camera · React Dashboard
Studio
Golden Sea Studios
Ho Chi Minh City, Vietnam
This project proves
AI & AutomationEdge computer vision on the factory floor: PPE detection, restricted-zone behaviour and sub-second fire alerts.
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