Hybrid kitchen safety guarding with stove fire recognition based on the Internet of Things

Abstract

In this paper, we design a hybrid kitchen safety guarding framework using embedded devices and onboard sensors to detect abnormal events and block gas sources in time through the Internet of Things (IoT). According to the relevant literature we studied, this is the first framework for kitchen safety guarding that provides the following features: (1) the deep learning based model integrating densely connected convolutional networks with neural architecture search networks is developed to accurately recognize abnormal stove fire, (2) the acceleration correction method is designed to correct the sensed accelerometer values for estimating the actual earthquake level, and (3) the proper gas leakage threshold is defined to precisely detect the gas leakage for automatic gas blocking, and remote surveillance and control are provided to monitor the kitchen environment and control the gas source anytime and anywhere. In particular, an Android-based prototype consisting of the IoT device, diverse sensors, dedicated server, and smartphones is implemented to verify the feasibility and superiority of our framework. Experimental results show that our framework outperforms existing methods and can precisely recognize stove fire intensity and detect earthquake levels for kitchen safety guarding.