Multimodal RGB-Infrared Combination for UAV-Based Wildfire Segmentation: A Comparative Study on FLAME3

2026-09-01

AI Summary

研究通过比较RGB和红外基线及三种融合策略在U-Net、DeepLabV3+、SegFormer上的表现，探讨了多模态信息融合对无人机野火分割的影响，指出热成像信息与基于变压器架构的特征级融合最具潜力。

Abstract

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.

Problem

• UAV
• wildfire segmentation
• multimodal fusion
• RGB-infrared

Innovation

• RGB-infrared fusion
• wildfire segmentation
• multimodal information
• transformer-based architectures
• feature-level fusion