Wildfire Detection Via Transfer Learning: A Survey

• URL: http://arxiv.org/abs/2306.12276v1
• Date: Wed, 21 Jun 2023 13:57:04 GMT
• Title: Wildfire Detection Via Transfer Learning: A Survey
• Authors: Ziliang Hong, Emadeldeen Hamdan, Yifei Zhao, Tianxiao Ye, Hongyi Pan, A. Enis Cetin
• Abstract summary: 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.
• Score: 2.766371147936368
• License: http://creativecommons.org/licenses/by/4.0/
• 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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