| Abstract | One of the most deadly situations that firefighters could face in firefighting is flashover, which is sudden fire propagation occurring in a room with all the items in the room bursting into the fire. In general, firefighters need years of training to identify and predict the flashover. Although the decades of experimental and numerical fire research shed light on the room fire dynamics, there are still gaps in transferring the fire science to the fire ground where innovative yet simple solutions are needed to overcome the harsh environment.
This project is to develop a robust smart firefighting tool that can be easily deployed like cameras to the fire ground and provide effective assistance to firefighters. One key ability of the smart fighting tool would be assisting firefighters in detecting impending deadly flashovers. An explorative study is conducted adopting deep learning methods in the processing of smoke and flame video images. Scientific knowledge of room fires is also coupled to build an algorithm that requires less hardware but produces high accuracy. The hybrid system combining deep learning methods and fire safety knowledge only requires RGB vision data for flashover prediction, which can be acquired by any camera used by firefighters. The system converts the RGB inputs to thermal images and processes the flashover analysis with images classified as smoke and flame.
The system was tested with video data obtained from various fire tests, and the performance was evaluated and compared with other existing models. The hybrid algorithm of the flashover prediction system demonstrated promising performance by surpassing other existing methods designed for similar tasks, with high prediction accuracy |
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