基于无人机的野火分割的多模态RGB-红外组合：FLAME3数据集上的对比研究
Multimodal RGB-Infrared Combination for UAV-Based Wildfire Segmentation: A Comparative Study on FLAME3
- University of Coimbra（科英布拉大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究在FLAME3数据集上对比了不同融合策略与分割架构，发现热信息主导无人机野火分割，特征级多模态融合结合Transformer架构最具研究前景。
AI中文摘要：
无人机（UAV）因灵活性高、运营成本低，且能在传统方法难以或危险进入的区域获取高分辨率图像，已成为消防作业的有前景平台。深度学习的最新进展显著提升了基于无人机的野火监测系统的能力。本研究针对FLAME3数据集上的二值野火分割任务，探究RGB-红外融合方法，将RGB、红外基线与三种代表性融合策略在U-Net、DeepLabV3+、SegFormer三种分割架构上进行对比。核心动机为分析各模态的贡献、评估融合时机的影响，以及探究不同网络架构如何利用多模态信息进行无人机野火轮廓描绘。研究发现，热信息在无人机分割中起主导作用，特征级多模态融合结合基于Transformer的架构是未来研究最具前景的方向。
英文摘要：
Unmanned Aerial Vehicles (UAVs) have emerged as a promising platform for firefighting operations due to their flexibility, low operational cost, and ability to acquire high-resolution imagery in locations that may be difficult or dangerous to access using conventional methods. Recent advances in deep learning have significantly improved the capabilities of UAV-based wildfire monitoring systems. The present work investigates RGB-infrared fusion for binary wildfire segmentation on the FLAME3 dataset. In this Study, RGB and Infrared baselines are compared with three representative fusion strategies across three segmentation architectures, including U-Net, DeepLabV3+, and SegFormer. The key motivation of this work is to analyze the contribution of each modality, evaluate the impact of fusion timing, and examine how different network architectures exploit multimodal information for UAV wildfire delineation. The findings indicate that thermal information plays a dominant role in UAV segmentation and that feature-level multimodal fusion combined with transformer-based architectures offers the most promising direction for future research.
