Semi-Supervised Domain Adaptation for Wildfire Detection
JooYoung Jang Youngseo Cha Jisu Kim SooHyung Lee Geonu Lee Minkook Cho Young Hwang Nojun Kwak
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.