> research.division / ai.fire.detection
Weteachmachinestoseefirebeforeitbecomesone.
CNN flame geometry. RNN smoke temporality. Multi-modal Bayesian fusion. Sub-second inference deployed to edge silicon — trained on two million labelled combustion events.
> overview
A research pipeline, not a product brochure.
The AI Fire Detection division develops machine learning models that redefine how fire events are identified, classified, and predicted — spanning convolutional networks for real-time flame recognition, recurrent networks for temporal smoke analysis, and sensor fusion algorithms that combine optical, thermal and electrochemical streams into a unified inference pipeline.
Training data covers more than two million labelled combustion events captured across controlled burn laboratories — smouldering, flaming and explosive scenarios across diverse fuels and conditions. VESDA AI integration extends the same models into predictive failure analytics for detection circuit degradation before hardware faults manifest.
By deploying multi-modal inference at the edge, we eliminate false alarms that plague threshold-based systems — a 97% reduction in nuisance events while sustaining sub-second response on genuine fire signatures.
> methodology
From raw burn telemetry to deployed firmware.
Data Acquisition & Labelling
Fire scenario data is captured from controlled burn laboratories, multi-spectral cameras, gas chromatography sensors, and real-world incident databases. Over 2 million labelled fire scenarios form the training corpus.
Model Architecture Design
Convolutional neural networks (CNNs) process video feeds for flame geometry recognition while recurrent neural networks (RNNs) analyse temporal smoke propagation patterns across sequential frames.
Sensor Fusion & Multi-Modal Inference
Optical, thermal, and gas sensor streams are fused through a Bayesian inference engine, dramatically reducing false alarm rates while maintaining sub-second detection latency.
VESDA AI Integration & Deployment
Trained models are deployed to edge devices integrated with VESDA (Very Early Smoke Detection Apparatus) systems, providing predictive failure analytics for detection circuit degradation.
Continuous Learning & Validation
Deployed models undergo continuous evaluation against new fire scenarios, with automated retraining pipelines ensuring detection accuracy improves over each operational cycle.
> research outcomes
Capabilities engineered, measured, deployed.
CNN Flame Detection
Video-based convolutional neural networks trained to classify flame geometry, colour temperature, and flicker frequency across benchmark datasets.
RNN Smoke Analysis
Recurrent neural network architectures model temporal smoke propagation dynamics, distinguishing between nuisance aerosols and genuine combustion products.
Sensor Fusion Engine
Multi-modal Bayesian inference combining optical density, infrared thermal signatures, and electrochemical gas sensor readings for false alarm elimination.
Predictive Circuit Analytics
Machine learning models forecast detection circuit degradation using impedance analysis, sensor drift tracking, and environmental compensation algorithms.
> standards / publications
Built against the standards that govern fire safety.
> collaborate
Bringusafiredetectionproblemworthsolving.
Research partnerships, model deployments, custom inference pipelines — speak with the team building it.