Wildfire Detection Via Transfer Learning: A Survey

Ziliang Hong, Emadeldeen Hamdan, Yifei Zhao, Tianxiao Ye, Hongyi Pan +1 more
6/21/2023
cs.CV

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Abstract

This paper surveys different publicly available neural network models used for detecting wildfires using regular visible-range cameras which are placed on hilltops or forest lookout towers. The neural network models are pre-trained on ImageNet-1K and fine-tuned on a custom wildfire dataset. The performance of these models is evaluated on a diverse set of wildfire images, and the survey provides useful information for those interested in using transfer learning for wildfire detection. Swin Transformer-tiny has the highest AUC value but ConvNext-tiny detects all the wildfire events and has the lowest false alarm rate in our dataset.

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Cite this paper

@article{hong2023wildfire,
  title  = {Wildfire Detection Via Transfer Learning: A Survey},
  author = {Ziliang Hong and Emadeldeen Hamdan and Yifei Zhao and Tianxiao Ye and Hongyi Pan and A. Enis Cetin},
  year   = {2023},
  eprint = {2306.12276},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.12276v1}
}

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