AI video analytics use case
AI fire and smoke detection from CCTV cameras
Short answer
AI fire and smoke detection is video analytics that recognizes the visual signs of smoke and flames in camera footage and raises an alert. It adds an early visual warning layer in open areas such as warehouses, yards and plant floors, where ceiling detectors can be sparse or slow. It supplements, and does not replace, certified fire alarm systems.
How fire and smoke detection works
A computer-vision model trained on images of smoke and fire analyzes each frame for their characteristic shapes, colors and movement, such as rising, spreading plumes or flickering flame regions. Detections are typically confirmed over several consecutive frames before an alert is raised, which helps filter out brief reflections or passing lights.
Rules restrict detection to the zones where it matters and can exclude areas with expected flames or steam, such as burners, exhausts or process stacks.
In OrionAlerts
In OrionAlerts, the AI runtime reads decoded frames from the same video engine that records the cameras, so detection runs on the streams you already record rather than on a parallel feed into a separate analytics appliance. Inference runs on the on-site Nebula OS node with GPU acceleration, and each event is written to the same record as the camera and site it came from. Alerts reach the dashboard, mobile app and email.
What affects reliability
Detection quality depends on the scene as much as the model. These are the factors to check during site survey and pilot.
- Camera placement: the camera must see the area where smoke or flames would first appear, without permanent obstructions.
- Lighting: glare, sunlight reflections and low light change how smoke and flames look in the image, and smoke is hard to see in darkness.
- Resolution and distance: small or distant smoke takes up few pixels and is harder to recognize.
- Scene conditions: steam, dust, fog and vehicle exhaust can resemble smoke and need to be accounted for in zones and rules.
What you need
No camera replacement is assumed. A typical OrionAlerts deployment needs:
- Existing IP cameras that expose video over ONVIF, RTSP or a supported vendor SDK. Compatibility is confirmed per camera model during solution design.
- An on-site Nebula OS node with GPU acceleration, sized per deployment to the channel count and the detections you run.
- Camera views that actually cover the area to be monitored, with zones and schedules defined for each rule.
- Optional: Orion Cloud for a central view of alerts and health across multiple sites, without uploading raw video.
- Video detection supplements, and does not replace, certified fire detection and alarm systems required by code.
Where it is used
This use case commonly applies in the industries below. Each industry page covers the risks, detections and modules that matter for that kind of site.
Solution
Which solution it belongs to
Fire and Smoke Detection is part of AI Vision & Real-Time Alerts: turn live video into real-time AI alerts for safety, intrusion, PPE, and operational events.
Explore AI Vision & Real-Time AlertsFAQ
Fire and Smoke Detection: common questions
Can AI fire and smoke detection replace smoke detectors?
No. Video-based fire and smoke detection is an additional early-warning layer, not a life-safety system. It does not replace the certified fire detection and alarm systems that building and fire codes require. Use it alongside them to add visual coverage of open areas, yards and zones where conventional detectors are sparse.
Does fire and smoke detection work on existing CCTV cameras?
Often, yes, if the camera has a clear view of the area where smoke or flames would first appear. OrionAlerts connects existing IP cameras through ONVIF, RTSP and vendor SDKs, and compatibility is confirmed per camera model during solution design. Cameras with obstructed or poorly lit views may need repositioning.
What causes false fire or smoke alerts?
Steam, dust, fog, vehicle exhaust, reflections and sudden lighting changes are the usual causes, because they can resemble smoke or flame in the image. Careful zone setup, excluding areas with expected flames or steam such as burners and stacks, and confirming detections over several frames all help reduce them.
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Plan this use case for your sites
We will review your camera views for this use case, scope the deployment for your sites and show it in a live demo.