Fire Detection and Safety Systems

Detecting fire and smoke reliably before a blaze spreads is one of the most consequential challenges in safety engineering, and researchers are increasingly turning to computer vision and deep learning to meet it. By training convolutional neural networks on video feeds from fixed cameras, forest monitoring stations, and unmanned aerial vehicles, scientists aim to identify the visual signatures of fire and smoke far faster and more accurately than traditional sensor-based alarms allow. A persistent difficulty is distinguishing genuine fire from visually similar phenomena—sunlight glare, steam, or autumn foliage—without generating the false alarms that cause systems to be ignored or disabled. Active research directions include fusing multiple visual features with environmental sensor data, improving detection under low-light or hazy conditions, and building lightweight models capable of running in real time on edge devices deployed in remote wilderness areas.

Works

35,764

Total citations

136,787

Keywords

Computer VisionFire DetectionSmoke DetectionConvolutional Neural NetworksVideo SurveillanceForest Fire Monitoring