Abstract 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.
Jia et al. (Thu,) studied this question.
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