Multistage Real-Time Fire Detection Using Convolutional Neural Networks and Long Short-Term Memory Networks

Fire is one of the most commonly occurring disasters and is the main cause of catastrophic personal injury and devastating property damage. An early detection system is necessary to prevent fires from spreading out of control. In this paper, we propose a multistage fire detection method using convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. In the first stage, fire candidates are detected by using their salient features, such as their color, flickering frequency, and brightness. In the second stage, a pretrained CNN model is used to extract the 2D features of flames that are the input for the LSTM network. In the last stage, a softmax classifier is utilized to determine whether the flames represent a true fire or a nonfire moving object. The experimental results show that our proposed method can achieve competitive performance compared with other state-of-the-art methods and is suitable for real-world applications.

Automated summary

The paper proposes a multistage fire detection method using convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, combining early detection of fire candidates and 2D features extraction to determine if it is a true fire, showing competitive performance in real-world applications.