A wavelet-based real-time fire detection algorithm with multi-modeling framework

This paper presents a wavelet-based real-time automated fire detection algorithm that takes into consideration the multi-resolution property of the wavelet transforms. Unlike conventional fire detection algorithms, which fail to capture temporal dependency within the fire sensor signals, the proposed wavelet-based features characterize temporal dynamics of chemical sensor signals generated from various types of fire, such as flaming, heating and smoldering fires. We propose a new feature selection technique based on types of fire to select the best features that can effectively discriminate between normal and various fire conditions. Then, a real-time fire detection algorithm with a multi-modeling framework is developed to effectively utilize the selected features and construct multiple fire detectors that are sensitive in monitoring various kinds of fires without prior knowledge. In addition, we develop a novel multi-sensor fusion system that incorporates various chemical sensors and collects an accurate and reliable fire dataset from different real-life fire scenarios in order to validate the performance of the proposed and existing fire detection algorithms. The experimental results with real-life and public fire data show that the proposed algorithm outperforms others with early detection time with a reasonable false alarm rate regardless of the type of fire.