AI system detects fires before alarms sound, NYU study shows

NYU research introduces video-based fire detection

The NYU Tandon School of Engineering has reported that its Fire Research Group has developed an artificial intelligence system that can detect fires and smoke in real time using existing CCTV cameras.

According to NYU Tandon, the system analyses video frames within 0.016 seconds, faster than a human blink, and provides immediate alerts.

The researchers explained that conventional smoke alarms activate only once smoke has reached a sensor, whereas video analysis can recognise fire at an earlier stage.

Lead researcher Prabodh Panindre, Research Associate Professor at NYU Tandon’s Department of Mechanical and Aerospace Engineering, said: “The key advantage is speed and coverage.

“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.”

Ensemble AI approach improves accuracy

NYU Tandon explained that the system combines multiple AI models rather than relying on a single network.

It noted that this reduces the risk of false positives, such as mistaking a bright object for fire, and improves detection reliability across different environments.

The team reported that Scaled-YOLOv4 and EfficientDet models provided the best results, with detection accuracy rates above 78% and processing times under 0.02 seconds per frame.

By contrast, Faster-RCNN produced slower results and lower accuracy, making it less suitable for real-time IoT use.

Dataset covers all NFPA fire classes

According to the NYU researchers, the system was trained on a custom dataset of more than 7,500 annotated images covering all five fire classes defined by the National Fire Protection Association.

The dataset included Class A through K fires, with scenarios ranging from wildfires to cooking incidents.

This approach allowed the AI to generalise across different ignition types, smoke colours, and fire growth patterns.

The team explained that bounding box tracking across frames helped differentiate live flames from static fire-like objects, achieving 92.6% accuracy in reducing false alarms.

Professor Sunil Kumar of NYU Abu Dhabi said: “Real fires are dynamic, growing and changing shape.

“Our system tracks these changes over time, achieving 92.6% accuracy in eliminating false detections.”

Technical evaluation of detection models

NYU Tandon reported that it tested three leading object detection approaches: YOLO, EfficientDet and Faster-RCNN.

The group found that Scaled-YOLOv4 achieved the highest accuracy at 80.6% with an average detection time of 0.016 seconds per frame.

EfficientDet-D2 achieved 78.1% accuracy with a slightly slower response of 0.019 seconds per frame.

Faster-RCNN produced 67.8% accuracy and required 0.054 seconds per frame, making it less practical for high-throughput applications.

The researchers concluded that Scaled-YOLOv4 and EfficientDet-D2 offered the best balance of speed and reliability for real-world deployment.

Dataset preparation and training methods

The research team stated that it collected approximately 13,000 images, which were reduced to 7,545 after cleaning and annotation.

Each image was labelled with bounding boxes for fire and smoke, and the dataset was evenly distributed across the five NFPA fire classes.

The models were pre-trained on the Common Objects in Context dataset before being fine-tuned on the fire dataset for hundreds of training epochs.

The team confirmed that anchor box calibration and hyperparameter tuning further improved YOLO model accuracy.

They reported that Scaled-YOLOv4 with custom training configurations provided the best results for dynamic fire detection.

IoT cloud-based deployment

The researchers outlined that the system operates in a three-layer Internet of Things architecture.

CCTV cameras stream raw video to cloud servers where AI models analyse frames, confirm detections and send alerts.

Detection results trigger email and text notifications, including short video clips, using Amazon Web Services tools.

The group reported that the system processes frames in 0.022 seconds on average when both models confirm a fire or smoke event.

This design, they said, allows the system to run on existing “dumb” CCTV cameras without requiring new hardware.

Deployment framework and false alarm reduction

The NYU team explained that fire detections are validated only when both AI models agree and the bounding box area grows over time.

This approach distinguishes real flames from static images of fire, preventing common sources of false alerts.

The deployment is based on Amazon Web Services with EC2 instances handling video ingestion and GPU-based inference.

Results and metadata are stored in S3 buckets and notifications are sent through AWS SNS and SES channels.

The researchers stated that this cloud-based framework ensures scalability and consistency across multiple camera networks.

Applications in firefighting and wildland response

NYU Tandon stated that the technology could be integrated into firefighting equipment, such as helmet-mounted cameras, vehicle cameras and autonomous robots.

It added that drones equipped with the system could provide 360-degree views during incidents, assisting fire services in locating fires in high-rise buildings or remote areas.

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

The researchers noted that the system could also support early wildfire detection, giving incident commanders more time to organise resources and evacuations.

Broader safety applications

Beyond fire detection, the NYU group explained that the same AI framework could be adapted for other safety scenarios, including medical emergencies and security threats.

It reported that the ensemble detection and IoT architecture provide a model for monitoring and alerting in multiple risk environments.

Relevance for fire and safety professionals

For fire and rescue services, the system demonstrates how existing CCTV infrastructure can be adapted for early fire detection without requiring new sensors.

For building managers, the research shows how AI video analysis could supplement or back up smoke alarms, particularly in settings where detector failure is a risk.

For wildland and urban response teams, the ability to embed the system into drones or helmet cameras may improve situational awareness and decision-making during fast-developing incidents.

AI system uses CCTV to detect fires in real time: Summary

The NYU Tandon School of Engineering Fire Research Group has reported an AI system that detects fires using CCTV cameras.

The research was published in the IEEE Internet of Things Journal.

The system processes video at 0.016 seconds per frame.

Scaled-YOLOv4 achieved 80.6% accuracy and EfficientDet achieved 78.1% accuracy.

False detections were reduced by tracking bounding box changes over time.

The dataset included 7,545 images covering all five NFPA fire classes.

Alerts are generated in real time through AWS cloud systems.

Applications include CCTV monitoring, drones, firefighter equipment and wildland detection.

The research suggests the same framework could support wider emergency monitoring.