AI fire-detection system is 'faster than the blink of an eye'

US researchers have developed a fire safety system that combines AI and standard security cameras; they say it could transform emergency response.

In the US, fire kills nearly 3700 people annually and destroys $23 billion in property, with many deaths occurring because traditional smoke detectors fail to alert occupants in time.

To address this problem, the NYU Fire Research Group at NYU Tandon School of Engineering has developed an artificial intelligence system that could significantly improve fire safety by detecting fires and smoke in real time. A key feature of the system is that it uses ordinary security cameras that are already installed in many buildings.

Unlike conventional smoke detectors that require significant smoke build-up and proximity to activate, this AI system can spot fires in their earliest stages from video alone. It is able to analyse video footage and identify fires within 0.016 seconds per frame — faster than the blink of an eye — potentially providing crucial extra minutes for evacuation and emergency response.

“The key advantage is speed and coverage,” explained lead researcher Prabodh Panindre, Research Associate Professor in NYU Tandon’s Department of Mechanical and Aerospace Engineering (MAE).

“A single camera can monitor a much larger area than traditional detectors, and we can spot fires in the initial stages before they generate enough smoke to trigger conventional systems.”

The researchers’ findings have been published in the IEEE Internet of Things Journal.

The need for improved fire detection technology is evident from concerning statistics: 11% of residential fire fatalities in the US occur in homes where smoke detectors failed to alert occupants, either due to malfunction or the complete absence of detectors. Additionally, modern building materials and open floor plans have increased the speed that fires spread, with structural collapse times significantly reduced compared to legacy construction.

The research team developed an approach that combines multiple state-of-the-art AI algorithms. Rather than relying on a single AI model that might mistake a red car or sunset for fire, the system requires agreement between multiple algorithms before confirming a fire detection, substantially reducing false alarms.

The researchers trained their models by building a comprehensive custom image dataset representing all five classes of fires recognised by the National Fire Protection Association, from ordinary combustible materials to electrical fires and cooking-related incidents. The system achieved notable accuracy rates, with the best-performing model combination reaching 80.6% detection accuracy.

The system incorporates temporal analysis to differentiate between actual fires and static fire-like objects that could trigger false alarms. By monitoring how the size and shape of detected fire regions change over consecutive video frames, the algorithm can distinguish between a real, growing fire and a static image of flames hanging on a wall.

“Real fires are dynamic, growing and changing shape,” said Sunil Kumar, Professor of MAE. “Our system tracks these changes over time, achieving 92.6% accuracy in eliminating false detections.”

The technology operates within a cloud-based Internet of Things architecture where multiple standard security cameras stream raw video to servers that perform AI analysis. When fire is detected, the system automatically generates video clips and sends real-time alerts via email and text message. This design means the technology can be implemented using existing CCTV infrastructure without requiring expensive hardware upgrades — an advantage for widespread adoption.

Further uses of the technology include integration into drones or unmanned aerial vehicles to search for wildfires in remote forested areas. Early-stage wildfire detection would buy critical hours in the race to contain and extinguish fires, enabling faster dispatch of resources, as well as prioritised evacuation orders that dramatically reduce ecological and property loss.

The same detection system can also be embedded into the tools firefighters already carry: helmet cameras, thermal imagers and vehicle-mounted cameras, as well as into autonomous firefighting robots. In urban areas, UAVs integrated with this technology can help the fire service to perform a 360-degree size-up, especially when fire is on the higher floors of high-rise structures.

“It can remotely assist us in confirming the location of the fire and possibility of trapped occupants,” said Captain John Ceriello from the Fire Department of New York City.

Beyond fire detection, the researchers noted that their approach could be adapted for other emergency scenarios such as security threats or medical emergencies.

The team’s study, ‘Artificial Intelligence-Integrated Autonomous IoT Alert System for Real-Time Remote Fire and Smoke Detection in Live Video Streams’, can be read at DOI: 10.1109/JIOT.2025.3598979.

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