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.