Vision Sensor-Based Real-Time Fire Detection in Resource-Constrained IoT Environments

Fire detection and management is very important to prevent social, ecological, and economic damages. However, achieving real-time fire detection with higher accuracy in an IoT environment is a challenging task due to limited storage, transmission, and computation resources. To overcome these challen...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2021, Vol.2021 (1), p.5195508-5195508
Main Authors: Yar, Hikmat, Hussain, Tanveer, Khan, Zulfiqar Ahmad, Koundal, Deepika, Lee, Mi Young, Baik, Sung Wook
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Artificial neural networks
Datasets
Disasters
Equipment and supplies
False alarms
Feature extraction
Fire damage
Fire detection
Fire prevention
Forest & brush fires
Image processing
Model accuracy
Neural networks
Neural Networks, Computer
Property damage
Real time
Sensors
Surveillance
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Summary:Fire detection and management is very important to prevent social, ecological, and economic damages. However, achieving real-time fire detection with higher accuracy in an IoT environment is a challenging task due to limited storage, transmission, and computation resources. To overcome these challenges, early fire detection and automatic response are very significant. Therefore, we develop a novel framework based on a lightweight convolutional neural network (CNN), requiring less training time, and it is applicable over resource-constrained devices. The internal architecture of the proposed model is inspired by the block-wise VGG16 architecture with a significantly reduced number of parameters, input size, inference time, and comparatively higher accuracy for early fire detection. In the proposed model, small-size uniform convolutional filters are employed that are specifically designed to capture fine details of input fire images with a sequentially increasing number of channels to aid effective feature extraction. The proposed model is evaluated on two datasets such as a benchmark Foggia’s dataset and our newly created small-scaled fire detection dataset with extremely challenging real-world images containing a high-level of diversity. Experimental results conducted on both datasets reveal the better performance of the proposed model compared to state-of-the-art in terms of accuracy, false-positive rate, model size, and running time, which indicates its robustness and feasible installation in real-world scenarios.
ISSN:1687-5265
1687-5273
DOI:10.1155/2021/5195508