Smart City Gnosys

Smart city article details

Title	Visual Intelligence In Smart Cities: A Lightweight Deep Learning Model For Fire Detection In An Iot Environment
ID_Doc	61276
Authors	Nadeem M.; Dilshad N.; Alghamdi N.S.; Dang L.M.; Song H.-K.; Nam J.; Moon H.
Year	2023
Published	Smart Cities, 6, 5
DOI	http://dx.doi.org/10.3390/smartcities6050103
Abstract	The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection. © 2023 by the authors.
Author Keywords	deep learning; disaster management; fire classification; fire monitoring; internet of things; lightweight model; MobileNet; smart cities

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