FLAME2-DT

FLAME2-DT (Forest Fire Detection Dataset with Dual-modality Labels) is a comprehensive multi-modal dataset specifically designed for UAV-based forest fire detection research. The dataset consists of 1,280 paired RGB-thermal infrared images captured by a Mavic 2 Enterprise Advanced UAV system, with high-resolution (640×512) and precise pixel-level annotations for both fire and smoke regions. This dataset addresses critical challenges in forest fire detection by providing paired multi-modal data that captures the complementary characteristics of visible light and thermal imaging. The RGB images contain 2,496 fire bounding boxes and 4,404 smoke bounding boxes, while the thermal infrared images include 27,117 fire bounding boxes. Statistical analysis reveals distinct scale and distribution patterns: approximately 80% of fire regions occupy less than 5% of the image area with discrete distribution, while over 60% of smoke regions cover more than 12% with continuous patterns. FLAME2-DT is organized into five specialized packages to facilitate different research scenarios: original dataset, RGB-specific, thermal IR-specific, RGB with dual-modality labels, and complete fusion packages. The dataset is split into training (80%) and validation (20%) sets, providing a standardized benchmark for evaluating multi-modal forest fire detection algorithms. This dataset contributes to the advancement of forest fire detection research by: 1. Providing precisely registered multi-modal image pairs 2. Offering comprehensive pixel-level annotations verified through multi-expert cross-validation 3. Supporting the development of lightweight, real-time detection systems for UAV applications 4. Enabling comparative analysis of single-modal and multi-modal detection approaches

FLAME2-DT（双模态标签森林火灾检测数据集）是一个综合多模态数据集，专门为基于无人机（UAV）的森林火灾检测研究设计。该数据集包含1280组成对的RGB-热红外图像，由大疆Mavic 2 Enterprise Advanced无人机系统拍摄，具有640×512的高分辨率，并对火灾和烟雾区域进行了精确的像素级标注（pixel-level annotation）。该数据集通过提供成对多模态数据解决了森林火灾检测中的关键挑战，这些数据捕捉了可见光和热成像的互补特性。RGB图像包含2496个火灾边界框（bounding box）和4404个烟雾边界框，而热红外图像包含27117个火灾边界框。统计分析显示不同的尺度和分布模式：约80%的火灾区域占图像面积的不到5%，呈离散分布；而超过60%的烟雾区域占比超过12%，呈连续分布。FLAME2-DT被组织成五个专用包，以方便不同的研究场景：原始数据集、RGB专用包、热红外专用包、带双模态标签的RGB包以及完整融合包。数据集分为训练集（80%）和验证集（20%），为评估多模态森林火灾检测算法提供了标准化基准。该数据集通过以下方面推动森林火灾检测研究的进展：1. 提供精确配准的多模态图像对；2. 提供经过多专家交叉验证的全面像素级标注；3. 支持面向无人机应用的轻量级实时检测系统的开发；4. 实现单模态与多模态检测方法的对比分析。

搜集汇总

背景与挑战

背景概述

FLAME2-DT是一个用于无人机森林火灾检测的多模态数据集，包含1280对高分辨率RGB-热红外图像及精确的像素级标注，数据统计显示火灾和烟雾区域在尺度和分布上存在差异。数据集分为五个专用包和训练/验证集，支持多种火灾检测算法研究。