首个面向早期火灾监控的去烟数据集，助力真实场景下清晰成像。
SmokeBench: A Real-World Dataset for Surveillance Image Desmoking in Early-Stage Fire Scenes
- 构建真实火灾场景下带烟与无烟图像对，支持监督学习
- 涵盖多样场景与烟浓度，提供精确配准图像对
- 适合火灾监测、计算机视觉领域研究者使用
早期火灾场景（点火后0-15分钟）是应急干预的关键时段。此阶段燃烧产生的烟雾严重降低监控系统可见度，严重影响态势感知，阻碍救援行动。因此亟需从图像中去除烟雾以获取清晰场景信息。然而，由于缺乏大规模、真实世界中带有配对无烟与带烟图像的数据集，去烟算法发展受限。为此，我们提出名为SmokeBench的真实监控图像去烟基准数据集，包含在多种场景设置和烟浓度下采集的图像对。该数据集提供精确对齐的退化与清晰图像，支持监督学习与严格评估。我们在该数据集上对多种去烟方法进行了全面实验。本数据集为提升真实火灾场景下鲁棒且实用的图像去烟技术提供了重要基础。数据集已公开，可从https://github.com/ncfjd/SmokeBench下载。
原文摘要 · Abstract (English)
Early-stage fire scenes (0-15 minutes after ignition) represent a crucial temporal window for emergency interventions. During this stage, the smoke produced by combustion significantly reduces the visibility of surveillance systems, severely impairing situational awareness and hindering effective emergency response and rescue operations. Consequently, there is an urgent need to remove smoke from images to obtain clear scene information. However, the development of smoke removal algorithms remains limited due to the lack of large-scale, real-world datasets comprising paired smoke-free and smoke-degraded images. To address these limitations, we present a real-world surveillance image desmoking benchmark dataset named SmokeBench, which contains image pairs captured under diverse scenes setup and smoke concentration. The curated dataset provides precisely aligned degraded and clean images, enabling supervised learning and rigorous evaluation. We conduct comprehensive experiments by benchmarking a variety of desmoking methods on our dataset. Our dataset provides a valuable foundation for advancing robust and practical image desmoking in real-world fire scenes. This dataset has been released to the public and can be downloaded from https://github.com/ncfjd/SmokeBench.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。