RGB image-based hybrid model for automatic prediction of flashover in compartment fires

This paper proposes a novel hybrid model for flashover prediction in a compartment fire based on visual information from RGB images that are the same as those captured by regular vision cameras. The proposed model was developed as a research tool to study the feasibility of predicting flashover based on RGB vision data. This model consists of sub-modules with data-based methods using Deep Neural Networks and knowledge-based methods using fire safety science and mathematical model. One of the crucial features of the proposed model is enabled by a novel Dual-Attention Generative Adversarial Network that is developed in this study for the vision-to-infrared conversion process. The model and the overall procedure were validated against published test data from a compartment fire. Results show that the proposed model achieved promising performance, which also shows the potential to monitor the constant changes in a room fire through continuous processing images of flame and smoke.