An Edge-based Fire Detection System for Real-Time IoT Applications

Fire detection is a crucial research topic that has recently attracted many works. However, most of these existing methods tend to achieve high accuracy based on large deep neural networks without concern for real-time processing. Therefore, this paper proposes FireNet Lite, a lightweight CNN model optimized for real-time fire pattern recognition through efficient network design and pruning techniques. Experimental results show FireNet Lite achieves 96% accuracy on fire detection benchmarks while running at 36 fps on a Raspberry Pi 4, outperforming baseline deep neural networks. In addition, we also introduce a system that broadens the fire detection range by connecting all IoT devices with ThingsBoard.