Fire Detection and Safety Systems

Detecting fire and smoke reliably before a blaze spreads is one of the more consequential problems in modern safety engineering, and researchers have increasingly turned to computer vision and deep learning to replace or augment traditional sensor-based alarms. By training convolutional neural networks on video footage and aerial imagery, systems can identify the visual signatures of flames and smoke in real time, even across wide outdoor environments monitored by drones or fixed surveillance cameras. The core challenge is reducing false alarms triggered by visually similar phenomena—steam, fog, or reflections—while maintaining sensitivity early in a fire's development. Active research directions include improving detection robustness under variable lighting and weather conditions, integrating multi-sensor data streams, and deploying lightweight models capable of running on resource-constrained hardware aboard unmanned aerial vehicles.

Works

35,167

Total citations

134,186

Keywords

Computer VisionFire DetectionSmoke DetectionConvolutional Neural NetworksVideo SurveillanceForest Fire Monitoring