Predicting real-time fire heat release rate by flame images and deep learning

The heat release rate (HRR) is the most critical parameter in characterizing the fire behavior and thermal effects of a burning item. However, traditional fire calorimetry methods are not applicable due to the lack of equipment in most fire scenarios. This work explores the real-time fire heat release rate prediction by using fire scene images and deep learning algorithms. A big database of 112 fire tests from the NIST Fire Calorimetry Database is formed, and 69,662 fire scene images labeled by their transient heat release rate are adopted to train the deep learning model. The fire tests conducted in the lab environment and the real fire events are used to validate and demonstrate the reliability of the trained model. Results show that regardless of the fire sources, background, light conditions, and camera settings, the proposed AI-image fire calorimetry method can well identify the transient fire heat release rate using only fire scene images. This work demonstrates that the deep learning algorithms can provide an alternative method to measure the fire HRR when traditional calorimetric methods cannot be used, which shows great potential in smart firefighting applications.