Abstract
Fog computing assits the development of distributed real-time systems. This offers solutions to a quicker response systems for developing disaster monitoring, prevention and detection models into existence. This paper proposes the integration of Fog computing and Convolutional Neural Networks (CNN) with Unmanned Aerial Vehicles (UAV) to detect the forest fire at an early stage. A highly efficient CNN model has been used for fire image recognition due to its proven ability for such recognition tasks. By using AlexNet and other architectures in the proposed model, image recognition tasks have become more capable, to an extent that a pre-trained model has an ability equal to a primate. Using these architectures, we trained our model and deployed the same on a Fog device, which has resulted in achieving higher accuracy and response time.
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Srinivas, K., Dua, M. (2020). Fog Computing and Deep CNN Based Efficient Approach to Early Forest Fire Detection with Unmanned Aerial Vehicles. In: Smys, S., Bestak, R., Rocha, Á. (eds) Inventive Computation Technologies. ICICIT 2019. Lecture Notes in Networks and Systems, vol 98. Springer, Cham. https://doi.org/10.1007/978-3-030-33846-6_69
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DOI: https://doi.org/10.1007/978-3-030-33846-6_69
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