To ensure the safety of storage: enhancing accuracy of fire detection in warehouses with deep learning models

Abstract

Warehouses are important places with storage functions in process units, containing many flammable and explosive materials. Nevertheless, fire protection systems often fail to provide adequate information when fires break out, hindering firefighting and leading to catastrophic outcomes. This research aims to develop a deep learning (DL) model capable of precisely locating the fire source and estimating its intensity, providing graphical representations of fire information. We propose a novel Multi-head Attention-based Long Short Term Memory (MHA-LSTM) model and apply Computational Fluid Dynamics (CFD) to generate datasets for model training on warehouse environments. The results demonstrate losses on the validation set and test set are less than 0.003, and all errors are below 0.15, indicating that MHA-LSTM has good generalization ability for fires in various locations. Compared to 3 common DL models, such as LSTM, the loss of MHA-LSTM on the test set is reduced by at least 51.6%, greatly improves the accuracy of fire detection in complex scenarios. Finally, based on firefighters' situational awareness (SA) requirements, graphical examples of estimated fire information are generated to provide accurate and visualized fire data. This study utilizes artificial intelligence (AI) to overcome the limitations of complex internal structures in warehouses, offering a feasible approach to improve process safety and the accuracy of fire protection systems.