A Thermal-Based Fuel-Prediction Method for Intelligent Fire Extinguisher in an Indoor Environment

Abstract

Classification of fuel in the early stage of fire is important to choose the appropriate type of extinguisher for extinguishing fire. This paper proposes a method of fuel prediction based on heat information for intelligent fire extinguisher in an indoor environment. Fire flame in the early stage is first detected based on patterns of differences between consecutive thermal image frames in which temperature grows up rapidly and reveals a sharp positive slope. Then candidate flame boundaries are detected in the thermal image frames during the early stage, and boundary matching is performed among the frames. These matched boundaries are classified as fire flame and fuel class based on LSTM (Long short-term memory) for extinguisher selection. Experiments were performed with 300 samples for classification into four classes of fuel, and the results based on 9:1 training and testing ratio showed 92.142% accuracy.