Scholarlink

<p>Because no multimodal dataset was previously available for fire detection research, we developed the MmodalFire multimodal fire detection dataset for training and evaluation of indoor fire detection algorithms. This publicly available dataset includes video and physical sensing data for fire detection use. The dataset comprises 65 videos that simultaneously captured six physical sensing data types, including smoke density, temperature, and infrared and ultraviolet radiation at 5 μm, 4.4 μm, and 3.8 μm. All data were acquired using monitoring cameras and fire sensors deployed as part of a fire detection system that was carefully designed to cover all possible variations, including different wind velocities, illumination conditions, common interference types, and occlusions. All videos and corresponding physical sensing data sequences are labeled as either fire or non-fire sequences. Using the MmodalFire dataset, we evaluated four basic baseline fusion models and the proposed dynamic fusion models to provide a reference for multimodal fire detection research under controlled laboratory settings, promoting research on multimodal fire detection algorithms using controlled-setting data.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

MmodalFire: A Continuous Multimodal Dataset Comprising Video and Physical Sensing Data for Detecting Indoor Fires

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月19日

查看全文
  • Yang Jia,
  • Yihan Guo,
  • Yetang Chen,
  • Xinmeng Zhang,
  • Gang Wang,
  • Qixing Zhang

摘要

Because no multimodal dataset was previously available for fire detection research, we developed the MmodalFire multimodal fire detection dataset for training and evaluation of indoor fire detection algorithms. This publicly available dataset includes video and physical sensing data for fire detection use. The dataset comprises 65 videos that simultaneously captured six physical sensing data types, including smoke density, temperature, and infrared and ultraviolet radiation at 5 μm, 4.4 μm, and 3.8 μm. All data were acquired using monitoring cameras and fire sensors deployed as part of a fire detection system that was carefully designed to cover all possible variations, including different wind velocities, illumination conditions, common interference types, and occlusions. All videos and corresponding physical sensing data sequences are labeled as either fire or non-fire sequences. Using the MmodalFire dataset, we evaluated four basic baseline fusion models and the proposed dynamic fusion models to provide a reference for multimodal fire detection research under controlled laboratory settings, promoting research on multimodal fire detection algorithms using controlled-setting data.