A Multimodal ML-based Indoor Fire Detection and Alarm System Using Image Processing and IoT–based Environmental Sensing

Abstract

This paper proposes a design and experimentally implements of a multimodal machine learning (ML)-based indoor fire detection and alarm system which combines image processing and Internet-of-Things (IoT)-based environmental sensing for mitigating false alarm. The proposed system consists of two fire ML-based detecting models: the image processing model used for detecting a fire based on fire images captured by a camera, a sensing data processing model used for predicting a fire based on room environmental data. Additionally, a communication module (i.e. 4G/Wi-Fi) is equipped to use for interacting with the home-owner and the fire station to get a confirmation before activating any further actions. In the proposed model, detected results of both abovementioned detecting models are combined to enhance the reliability of the final decision (i.e. whether it is a true fire or false fire). A true fire is recognized when two detecting modules both detected that it is a fire. Otherwise, when only one detecting module recognized that it is a false fire, it is recognized as uncertain. In this case, it needs to be confirmed by the home-owner based on fire images captured by the camera and sent over the communication module. If it is recognized as a true fire, a true alarm is raised and an alarm message containing house’s location information is sent to the fire station for activating the emergency case of fire. Otherwise, the fire is recognized as a false alarm and then no action is taken. The numerical and experimental results show that the proposed system achieves a high accuracy of fire detection and avoids false alarms.