@article{ref1,
title="DeepFireNet: a real-time video fire detection method based on multi-feature fusion",
journal="Mathematical biosciences and engineering",
year="2020",
author="Zhang, Bin and Sun, Linkun and Song, Yingjie and Shao, Weiping and Guo, Yan and Yuan, Fang",
volume="17",
number="6",
pages="7804-7818",
abstract="This paper proposes a real-time fire detection framework DeepFireNet that combines fire features and convolutional neural networks, which can be used to detect  real-time video collected by monitoring equipment. DeepFireNet takes surveillance  device video stream as input. To begin with, based on the static and dynamic  characteristics of fire, a large number of non-fire images in the video stream are  filtered. In the process, for the fire images in the video stream, the suspected  fire area in the image is extracted. Eliminate the influence of light sources,  candles and other interference sources to reduce the interference of complex  environments on fire detection. Then, the algorithm encodes the extracted region and  inputs it into DeepFireNet convolution network, which extracts the depth feature of  the image and finally judges whether there is a fire in the image. DeepFireNet  network replaces 5×5 convolution kernels in the inception layer with two 3×3  convolution kernels, and only uses three improved inception layers as the core  architecture of the network, which effectively reduces the network parameters and  significantly reduces the amount of computation. The experimental results show that  this method can be applied to many different indoor and outdoor scenes. Besides, the  algorithm effectively meets the requirements for the accuracy and real-time of the  detection algorithm in the process of real-time video detection. This method has  good practicability.<p /> <p>Language: en</p>",
language="en",
issn="1547-1063",
doi="10.3934/mbe.2020397",
url="http://dx.doi.org/10.3934/mbe.2020397"
}