Automatic Recognition of Indoor Fire and Combustible Material with Material-Auxiliary Fire Dataset

Abstract

Early and timely fire detection within enclosed spaces notably diminishes the response time for emergency aid. Previous methods have mostly focused on singularly detecting either fire or combustible materials, rarely integrating both aspects, leading to a lack of a comprehensive understanding of indoor fire scenarios. Moreover, traditional fire load assessment methods such as empirical formula-based assessment are time-consuming and face challenges in diverse scenarios. In this paper, we collected a novel dataset of fire and materials, the Material-Auxiliary Fire Dataset (MAFD), and combined this dataset with deep learning to achieve both fire and material recognition and segmentation in the indoor scene. A sophisticated deep learning model, Dual Attention Network (DANet), was specifically designed for image semantic segmentation to recognize fire and combustible material. The experimental analysis of our MAFD database demonstrated that our approach achieved an accuracy of 84.26% and outperformed the prevalent methods (e.g., PSPNet, CCNet, FCN, ISANet, OCRNet), making a significant contribution to fire safety technology and enhancing the capacity to identify potential hazards indoors.