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

TL;DR

MmodalFire is a new dataset combining video and sensor data to help improve indoor fire detection systems.

Contribution

The paper introduces MmodalFire, the first publicly available multimodal dataset for fire detection research.

Findings

The dataset includes 65 videos with six physical sensing data types for fire detection.

Baseline and dynamic fusion models were evaluated using the dataset for reference in multimodal fire detection.

The dataset covers variations like wind velocity, lighting, and occlusions for realistic testing.

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.

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