Predicting Fire Heat Release Rate Using Deep Perceptual and Detail‐Aware Hybrid Feature Fusion From Early Smoke Signals

With urbanisation accelerating, predicting the heat release rate (HRR) of building fires using visual data has emerged as a pivotal research focus in the field of fire rescue. However, existing approaches face challenges, such as limited training data and complex models, which lead to suboptimal performance and slow inference speeds. To address these issues and adapt to the rapid morphological changes of smoke in dynamic fire environments, we propose a lightweight neural network prediction model based on adaptive pooling with channel information interaction (APCI). This model can achieve high precision while maintaining faster inference speed. Our approach employs simplified dense connections to propagate shallow smoke features, thereby effectively capturing the relationship between smoke textures and multiscale features to accommodate the variations of smoke morphologies. To mitigate the loss of smoke features caused by spatial misalignment and ventilation disturbances during downsampling, we introduce an adaptive weighted pooling mechanism that fully leverages the detailed information contained in the invoked smoke. Additionally, an enhanced channel shuffle operation in channel information interaction ensures effective cross‐level transfer to detail‐aware information exchange during sudden escalations in fire intensity in the hybrid feature fusion framework. Experiments on the smoke‐heat release rate dataset we created demonstrate that the proposed method can achieve a coefficient of determination of 0.937, a root mean square error (RMSE) of 23.0 kW, a mean absolute error (MAE) of 17.4 kW and with an inference time of 4.13 ms per image.