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Additive neural network for forest fire detection

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Abstract

In this paper, we introduce a video-based wildfire detection scheme based on a computationally efficient additive deep neural network, which we call AddNet. This AddNet is based on a multiplication-free vector operator, which performs only addition and sign manipulation operations. In this regard, we construct a dot product-like operation from the mf-operator and use it to define dense and convolutional feed-forwarding passes in AddNet. We train AddNet on images taken from forestry surveillance cameras. Our experiments show that AddNet can achieve a time-saving by 12.4% when compared to an equivalent regular convolutional neural network (CNN). Furthermore, the smoke recognition performance of AddNet is as good as regular CNNs and substantially better than binary-weight neural networks.

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Acknowledgements

The research is supported by National Science Foundation, (No. 1739396) 2017. We also thank NVIDIA company for providing a GPU and the developers of TensorFlow for publishing a so powerful machine learning library.

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Authors and Affiliations

  1. University of Illinois at Chicago, Chicago, USA

    Hongyi Pan, Diaa Badawi, Xi Zhang & Ahmet Enis Cetin

  2. Bilkent University, Ankara, Turkey

    Ahmet Enis Cetin

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  1. Hongyi Pan
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  2. Diaa Badawi
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  3. Xi Zhang
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  4. Ahmet Enis Cetin
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Correspondence to Hongyi Pan.

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Pan, H., Badawi, D., Zhang, X. et al. Additive neural network for forest fire detection. SIViP 14, 675–682 (2020). https://doi.org/10.1007/s11760-019-01600-7

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  • DOI: https://doi.org/10.1007/s11760-019-01600-7

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