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Computationally Efficient Wildfire Detection Method Using a Deep Convolutional Network Pruned via Fourier Analysis.

ABSTRACT: In this paper, we propose a deep convolutional neural network for camera based wildfire detection. We train the neural network via transfer learning and use window based analysis strategy to increase the fire detection rate. To achieve computational efficiency, we calculate frequency response of the kernels in convolutional and dense layers and eliminate those filters with low energy impulse response. Moreover, to reduce the storage for edge devices, we compare the convolutional kernels in Fourier domain and discard similar filters using the cosine similarity measure in the frequency domain. We test the performance of the neural network with a variety of wildfire video clips and the pruned system performs as good as the regular network in daytime wild fire detection, and it also works well on some night wild fire video clips.

SUBMITTER: Pan H

PROVIDER: S-EPMC7287837 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Computationally Efficient Wildfire Detection Method Using a Deep Convolutional Network Pruned via Fourier Analysis.

Pan Hongyi H Badawi Diaa D Cetin Ahmet Enis AE

Sensors (Basel, Switzerland) 20200520 10

In this paper, we propose a deep convolutional neural network for camera based wildfire detection. We train the neural network via transfer learning and use window based analysis strategy to increase the fire detection rate. To achieve computational efficiency, we calculate frequency response of the kernels in convolutional and dense layers and eliminate those filters with low energy impulse response. Moreover, to reduce the storage for edge devices, we compare the convolutional kernels in Fouri ...[more]

PMID: 32443739

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