An AI-powered video analytics system that detects flames and smoke in facilities as they appear and triggers automated alarms before a fire can spread.
Factories, warehouses, and other facilities are vulnerable to fire and smoke incidents, particularly during unattended periods such as nighttime operations, when delayed detection can cause severe property damage, downtime, and risk to human life.
MoreYeahs built an AI-powered fire and smoke detection system for organizations running these facilities, turning existing fixed cameras into an always-on early-warning layer instead of relying solely on traditional point sensors.
Traditional fire alarms and smoke sensors left coverage and response gaps that facility teams needed to close:
Limited Early-Stage Detection: traditional sensors often missed early-stage flames, localized smoke, or spark-driven ignition in large or visually complex spaces.
Coverage Gaps: sensor placement limitations reduced early warning capability across sprawling facilities.
Delayed Verification: without visual confirmation, teams lost time distinguishing real incidents from false triggers.
High-Stakes Unattended Hours: risk was greatest during nighttime and unattended operations, when detection delay had the most severe consequences.
MoreYeahs built an AI-based fire and smoke detection system that uses fixed cameras to continuously monitor indoor and outdoor environments for visible signs of fire.
Flame and Smoke Recognition: computer vision and ML models analyze video streams for visible flames, smoke plumes, and flicker patterns associated with fire events.
Multi-Frame Validation: temporal pattern analysis checks flicker frequency and spread behavior to distinguish real fire from steam, dust, or reflected light.
Instant Multi-Channel Alerts: confirmed incidents trigger alarms, SMS, mobile notifications, and dashboard warnings simultaneously.
Automatic Evidence Capture: event snapshots and video clips are recorded for rapid verification by facility managers and emergency teams.
The pipeline runs continuously from live camera ingestion through confidence scoring, alerting, and dashboard visualization.
Continuous Frame Analysis: cameras stream video to the analytics engine, which scores flame and smoke features on every frame.
Confidence-Based Classification: a detection confidence score determines when an event is classified as an incident and an alert is raised.
Zone-Aware Alerting: each alert carries the camera ID and zone so response teams know exactly where to act.
Camera Health Monitoring: if a camera goes offline, the system raises a health alert and marks the zone as temporarily unmonitored.
The solution is built on fixed surveillance cameras paired with purpose-built fire and smoke detection models.
In validation testing, the system met its target benchmarks for detection accuracy and alert speed.
Turning existing camera infrastructure into an active fire detection layer gives facility teams confidence during the hours they're least staffed.
Faster Emergency Response: automated multi-channel alerts shrink the gap between ignition and human awareness.
Reduced Damage and Downtime: earlier detection limits how far a fire or smoke event can spread before intervention.
Fewer False Alarms: multi-frame and flicker validation cuts nuisance alerts from steam, dust, and glare.
Extends Existing Infrastructure: the system layers onto cameras facilities already have, rather than requiring new sensor networks.
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