"RGB-NIR Flame dataset"

"We propose a high-quality dual-channel (RGB-NIR) flame dataset to address the issues of low resolution, insufficient diversity, limited annotation quality, and scarcity of multispectral data in existing flame detection datasets. The dataset is constructed using a dual-camera acquisition platform, capturing high-resolution synchronized RGB-NIR image pairs that encompass seasonal variations in flame morphology. It also includes challenging non-flame samples such as vehicle headlights, streetlamps, and sunlight reflections to enhance discrimination capability in real-world scenarios. All images undergo rigorous modality registration and are annotated in PASCAL VOC format."

本研究提出一款高质量双通道（RGB-NIR）火焰数据集，旨在解决现有火焰检测数据集存在的分辨率偏低、多样性不足、标注质量有限以及多光谱数据稀缺等问题。该数据集通过双相机采集平台构建，采集了高分辨率同步RGB-NIR图像对，涵盖火焰形态的季节变化特征。数据集还纳入了车辆大灯、路灯、阳光反射等具有挑战性的非火焰样本，以提升模型在真实场景中的判别能力。所有图像均经过严格的模态配准，并采用PASCAL VOC格式进行标注。

搜集汇总

背景与挑战

背景概述

RGB-NIR Flame dataset是一个高质量的双通道火焰数据集，包含高分辨率的同步RGB-NIR图像对和多样化的非火焰样本，旨在提升火焰检测和误报抑制的能力。所有图像均经过严格配准和PASCAL VOC格式注释。