Explainable deep learning for image-driven fire calorimetry

The rapid advancement of deep learning and computer vision has driven the intelligent evolution of fire detection, quantification and fighting, although most AI models remain opaque black boxes. This work applies explainable deep learning methods to quantify fire power by flame images and aims to elucidate the underlying mechanism of computer-vision fire calorimetry. The process begins with the utilization of a pre-trained fire segmentation model to create a flame image database in various formats: (1) original RGB, (2) background-free RGB, (3) background-free grey, and (4) background-free binary. This diverse database accounts for factors such as background, color, and brightness. Then, the synthetic database is employed to train and test the fire-calorimetry AI model. Results highlight the dominant role of determining flame area in fire calorimetry, while other factors displaying minimal influence. Enhancing the accuracy of flame segmentation significantly reduces error in computer-vision fire calorimetry to less than 20%. Finally, the study incorporates the Gradient-weighted Class Activation Mapping (Grad-CAM) method to visualize the pixel-level contribution to fire image identification. This research deepens the understanding of vision-based fire calorimetry and providing scientific support for future AI applications in fire monitoring, digital twin, and smart firefighting.