Fire Detection Systems Using Feature Entropy Guided Neural Network

S K. Ahmed Mohiddin; I T V V S N S Pravallica; K. Pujitha; D. Nandini; S. Preetham

doi:10.32628/CSEIT2410287

Authors

S K. Ahmed Mohiddin Associate Professor, Department of CSE, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
I T V V S N S Pravallica Department of CSE, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
K. Pujitha Department of CSE, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
D. Nandini Department of CSE, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
S. Preetham Department of CSE, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT2410287

Keywords:

Fire Detection, Convolutional Neural Network, Surveillance

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.

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