Wildfire Detection Via Transfer Learning: A Survey
Ziliang Hong Emadeldeen Hamdan Yifei Zhao Tianxiao Ye Hongyi Pan A. Enis Cetin
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.