开源大规模野火图像视频数据集，助力早期烟雾火焰检测
A Large Scale Open-Source Image and Video Dataset for Robust Wildfire Detection and Classification
- 构建全球多场景野火数据集，含烟雾、火焰、红外等复杂条件
- 跨数据集测试显示模型具备强泛化能力，实测适用于真实监测
- 提出轻量级特征融合结构，提升模型在域偏移下的鲁棒性
野火探测与监测对控制火势蔓延、减少环境与基础设施损失至关重要。本文推出全球野火防控数据集（GWFP），一个大规模、开源的野火图像与视频数据集，旨在支持早期火灾与烟雾检测研究。该数据集涵盖全球真实场景下多种地理分布的野火画面，包括火焰、烟雾、水汽/雾、近红外影像、飞溅火星及挑战性负样本。为评估数据集的鲁棒性与跨领域泛化能力，我们在多个卷积与基于变压器的架构上进行了跨域与域内基准测试。此外，我们引入轻量级频率-空间特征交互机制，通过哈达玛增强残差连接（HTE-ResNet）分析域偏移下的表征稳定性。实验表明，模型具备优异的跨数据集泛化性能，具有实际应用价值。数据集与源码将在论文录用后公开。
原文摘要 · Abstract (English)
Wildfire detection and monitoring are critical for mitigating fire spread and reducing environmental and infrastructural damage. In this work, we introduce GWFP (Global Wildfire Prevention Dataset), a large-scale, open-source dataset of wildfire images and videos designed to support early fire and smoke detection research. GWFP contains geographically diverse wildfire scenes, including flames, smoke, Waterdog/Fog environmental conditions, Near Infrared (NIR) imagery, Ember, and challenging negative samples collected from real-world scenarios worldwide. To evaluate dataset robustness and cross-domain generalization, we benchmark multiple convolutional and transformer-based architectures across both in-domain and cross-dataset settings. Additionally, we explore lightweight frequency--spatial feature interaction using Hadamard-enhanced residual connections (HTE-ResNet) to analyze representation robustness under domain-shift conditions. Experimental results demonstrate strong cross-dataset generalization and practical utility for real-world wildfire monitoring applications. The dataset and source code will be publicly released upon acceptance.
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