Semi-Supervised Domain Adaptation for Wildfire Detection
This addresses wildfire detection for environmental monitoring and safety, but it is incremental as it builds on existing domain adaptation and object detection techniques.
The paper tackles wildfire detection by proposing a semi-supervised domain adaptation method that uses only 1% labeled target data, achieving a 3.8% improvement in mean Average Precision over a baseline on the HPWREN dataset.
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.