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.

翻译：本文综述了多种公开的神经网络模型，这些模型被用于通过安装在山顶或森林瞭望塔上的常规可见光摄像头检测野火。这些神经网络模型在ImageNet-1K上进行了预训练，并在自定义野火数据集上进行了微调。这些模型的性能在一组多样化的野火图像上进行了评估，该综述为那些对使用迁移学习进行野火检测感兴趣的人提供了有用的信息。Swin Transformer-tiny具有最高的AUC值，而ConvNext-tiny在我们的数据集中检测到了所有的野火事件，并拥有最低的误报率。