تقرير
Semi-Supervised Domain Adaptation for Wildfire Detection
| العنوان: | Semi-Supervised Domain Adaptation for Wildfire Detection |
|---|---|
| المؤلفون: | Jang, JooYoung, Cha, Youngseo, Kim, Jisu, Lee, SooHyung, Lee, Geonu, Cho, Minkook, Hwang, Young, Kwak, Nojun |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: 16 pages, 5 figures, 22 tables |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.01842 |
| رقم الأكسشن: | edsarx.2404.01842 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |