Recently, both the frequency and intensity of wildfires have increased worldwide, primarily due to climate change. In this paper, we propose a novel protocol for wildfire detection, leveraging semi-supervised Domain Adaptation for object detection, accompanied by a corresponding dataset designed for use by both academics and industries. Our dataset encompasses 30 times more diverse labeled scenes for the current largest benchmark wildfire dataset, HPWREN, and introduces a new labeling policy for wildfire detection. Inspired by CoordConv, we propose a robust baseline, Location-Aware Object Detection for Semi-Supervised Domain Adaptation (LADA), utilizing a teacher-student based framework capable of extracting translational variance features characteristic of wildfires. With only using 1% target domain labeled data, our framework significantly outperforms our source-only baseline by a notable margin of 3.8% in mean Average Precision on the HPWREN wildfire dataset. Our dataset is available at https://github.com/BloomBerry/LADA.

翻译：近年来，由于气候变化，全球范围内野火的频次和强度均有所增加。本文提出了一种新的野火检测方案，利用半监督域适应技术实现目标检测，并配套发布了可供学术界和工业界使用的数据集。该数据集包含当前最大的基准野火数据集HPWREN中30倍以上的多样化标注场景，并引入了新的野火检测标注策略。受CoordConv启发，我们提出了一个稳健的基线模型——面向半监督域适应的位置感知目标检测（LADA），该模型采用基于师生框架的结构，能够提取野火特有的平移变异特征。在仅使用1%目标域标注数据的情况下，我们的框架在HPWREN野火数据集上相较于仅使用源域数据的基线模型，平均精度均值显著提升了3.8%。数据集地址：https://github.com/BloomBerry/LADA。