A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Wildfires have emerged as one of the most destructive natural disasters worldwide, causing
catastrophic losses. These losses have underscored the urgent need to improve public …
catastrophic losses. These losses have underscored the urgent need to improve public …
[HTML][HTML] RNN-LSTM: From applications to modeling techniques and beyond—Systematic review
Abstract Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN)
algorithm known for its ability to effectively analyze and process sequential data with long …
algorithm known for its ability to effectively analyze and process sequential data with long …
Detection of forest fire using deep convolutional neural networks with transfer learning approach
Forest fires caused by natural causes such as climate change, temperature increase,
lightning strikes, volcanic activity or human effects are among the world's most dangerous …
lightning strikes, volcanic activity or human effects are among the world's most dangerous …
A wavelet-based real-time fire detection algorithm with multi-modeling framework
This paper presents a wavelet-based real-time automated fire detection algorithm that takes
into consideration the multi-resolution property of the wavelet transforms. Unlike …
into consideration the multi-resolution property of the wavelet transforms. Unlike …
Development of an early fire detection technique using a passive infrared sensor and deep neural networks
KLBL Xavier, VK Nanayakkara - Fire Technology, 2022 - Springer
Early detection of fire is key to mitigate fire related damages. This paper presents a
differential pyro-electric infrared (PIR) sensor and deep neural networks (DNNs) based …
differential pyro-electric infrared (PIR) sensor and deep neural networks (DNNs) based …
Variational AutoEncoders-LSTM based fault detection of time-dependent high dimensional processes
In modern large-scale industrial processes, data are often high dimensional time-dependent
due to the frequent sampling, dynamic nature and large number of variables. Appropriate …
due to the frequent sampling, dynamic nature and large number of variables. Appropriate …
Recurrent trend predictive neural network for multi-sensor fire detection
We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire
detection based on the trend as well as level prediction and fusion of sensor readings. The …
detection based on the trend as well as level prediction and fusion of sensor readings. The …
Field detection of indoor fire threat situation based on LSTM-Kriging network
X Cao, K Wu, X Geng, Q Guan - Journal of Building Engineering, 2024 - Elsevier
Obtaining the fire development information quickly and accurately is an important part of
indoor fire emergency evacuation and rescue. However, at present, buildings only use fire …
indoor fire emergency evacuation and rescue. However, at present, buildings only use fire …
Uncertainty assessment-based active learning for reliable fire detection systems
YJ Kim, WT Kim - IEEE Access, 2022 - ieeexplore.ieee.org
Deep learning technologies, due to their advanced pattern extraction and recognition of high-
dimensional data, have been widely adopted into multisensor-based fire detection systems …
dimensional data, have been widely adopted into multisensor-based fire detection systems …
An efficient firefighting method for robotics: A novel convolution-based lightweight network model guided by contextual features with dual attention
J Zhao, W Li, J Zhu, Z Gao, L Pan, Z Liu - Computers in Industry, 2024 - Elsevier
Efficient firefighting operations are crucial for ensuring the safety of firefighters and
preventing direct exposure to high-temperature and high-radiation environments. However …
preventing direct exposure to high-temperature and high-radiation environments. However …