Fire Detection and Safety Systems

Automated fire and smoke detection research applies computer vision and deep learning—particularly convolutional neural networks—to identify fire events from live video feeds, aerial imagery captured by drones, and distributed sensor networks before a blaze grows beyond control. The practical stakes are high: conventional heat or ionization detectors respond only when fire is already nearby, whereas image-based systems can flag early warning signs across wide or remote areas, from dense forests to industrial facilities. Researchers are actively working to reduce false alarms triggered by fire-like colors or lighting conditions, improve detection speed on resource-constrained hardware, and fuse multiple signals—color, texture, motion, and thermal data—into more reliable models. Open challenges include generalizing systems trained on one environment to perform well in another, and coordinating real-time detection across networks of unmanned aerial vehicles operating in dynamic, unpredictable conditions.

Works

34,653

Total citations

131,504

Keywords

Computer VisionFire DetectionSmoke DetectionConvolutional Neural NetworksVideo SurveillanceForest Fire Monitoring