MmodalFire:一个连续的多模式数据集,包括用于检测室内火灾的视频和物理传感数据
Yang Jia1,2, Yihan Guo1,2, Yetang Chen1,2
1Shaanxi Key Laboratory of Network Data Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China.
Scientific data
|February 19, 2026
概括
研究人员开发了MmodalFire数据集,用于多式联运火灾检测. 该数据集有助于训练和评估使用视频和传感器数据的室内火灾检测算法.
科学领域:
- 计算机科学 计算机科学
- 工程 工程师 工程师 工程师
- 安全科学安全科学 安全科学
背景情况:
- 多模式数据集对于推进火灾检测技术至关重要.
- 现有的数据集缺乏用于强大的算法开发所需的全面,同步的数据.
研究的目的:
- 介绍MmodalFire数据集,这是一个用于室内火灾检测研究的新型多式联运资源.
- 为训练和评估火灾检测算法提供标准化的基准.
主要方法:
- 收集了65个同步视频和6种物理传感器数据 (烟雾密度,温度,红外/紫外辐射).
- 通过改变风速,照明,干扰和遮蔽来确保数据多样性.
- 将所有数据序列标记为火或非火.
主要成果:
- "MmodalFire"数据集可以全面评估多式联络火灾检测.
- 在数据集上测试了基线融合模型和新的动态融合模型.
- 在受控环境中建立了多式联运火灾检测的性能基准.
结论:
- MmodalFire数据集解决了在火灾检测研究中对多式联运数据的需求.
- 促进了先进的火灾检测算法的开发和验证.
- 促进多式联通传感器融合领域的进一步研究,以提高消防安全.
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