Version 1
: Received: 2 October 2023 / Approved: 2 October 2023 / Online: 3 October 2023 (08:52:35 CEST)
How to cite:
Malebary, S. J. Early Fire Detection using LSTM based Instance Segmentation and IoTs for Disaster Management. Preprints2023, 2023100065. https://doi.org/10.20944/preprints202310.0065.v1
Malebary, S. J. Early Fire Detection using LSTM based Instance Segmentation and IoTs for Disaster Management. Preprints 2023, 2023100065. https://doi.org/10.20944/preprints202310.0065.v1
Malebary, S. J. Early Fire Detection using LSTM based Instance Segmentation and IoTs for Disaster Management. Preprints2023, 2023100065. https://doi.org/10.20944/preprints202310.0065.v1
APA Style
Malebary, S. J. (2023). Early Fire Detection using LSTM based Instance Segmentation and IoTs for Disaster Management. Preprints. https://doi.org/10.20944/preprints202310.0065.v1
Chicago/Turabian Style
Malebary, S. J. 2023 "Early Fire Detection using LSTM based Instance Segmentation and IoTs for Disaster Management" Preprints. https://doi.org/10.20944/preprints202310.0065.v1
Abstract
Fire outbreaks continue to cause damage despite the improvements in fire-detection tools and algorithms. It is still challenging to implement a well performing and optimized approach, which is sufficiently accurate, and has tractable complexity and low false alarming rate. Small amount of fire and identification of fire from a long distance is also a challenge in previously proposed techniques. In this study, we propose a novel hybrid model based on Convolutional Neural Networks (CNN) to detect and analyze fire intensity. 21 convolutional layers, 24 Rectified Linear Unit (ReLU) layers, 6 pooling layers, 3 fully connected layers, 2 dropout layers, and a softmax layer are included in the proposed 57-layer CNN model. Our proposed model performs instance segmentation in order to distinguish between fire and non-fire events. To reduce the intricacy of the proposed model, we also propose a key-frame extraction algorithm. The proposed model uses Internet of Things (IoT) devices to alert the relevant person by calculating the severity of fire. Our proposed model is tested on a publicly available dataset having fire and normal videos. The achievement of 95.25 % classification accuracy, 0.09% False Positive Rate (FPR), 0.65 percent False Negative Rate (FNR), and a prediction time of 0.08 seconds validates the proposed system.
Keywords
Instance Segmentation; Key-frame Extraction; Fire detection; IoTs; Disaster Management
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
