Deep Convolutional Neural Network for Fire Detection

Abstract

Fire detection from video has become possible and more feasible in prevention of fire disaster due to deep convolutional neural networks (CNNs) and embedded processing hardware. Artificial intelligence (AI) methods generally require more computational time and hardware with powerful graphical processing unit (GPU). In this paper, we propose cost-effective deep CNN architecture for fire detection from video with respect to computational performance of Jetson Nano from NVIDIA. In our paper we compare CNN networks (AlexNet and SqueezeNet) with our proposed CNN architecture. The proposed CNN architecture finds equilibrium between efficiency and accuracy for target system (Jetson Nano). We used CNNs which show high accuracy and low loss.