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arXiv · 2306.12276

Wildfire Detection Via Transfer Learning: A Survey

Ziliang Hong·Emadeldeen Hamdan·Yifei Zhao·Tianxiao Ye·Hongyi Pan·A. Enis Cetin
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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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Ziliang Hong, Emadeldeen Hamdan, Yifei Zhao, Tianxiao Ye, Hongyi Pan, A. Enis Cetin. 2023-06-21. Wildfire Detection Via Transfer Learning: A Survey. https://arxiv.org/abs/2306.12276

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