DeepSafe: A Hybrid Kitchen Safety Guarding System with Stove Fire Recognition Based on the Internet of Things

Abstract

This paper designs and implements a deep learning based hybrid kitchen safety guarding system, called DeepSafe, using embedded devices and onboard sensors to detect abnormal events and block gas sources in time through the Internet of Things (IoT). In the sensing mode, the DeepSafe system can prevent the kitchen from fire/explosion disasters by detecting gas concentration, recognizing fire intensity, and estimating vibration levels. In the control mode, the DeepSafe system can automatically block the gas source as detecting an abnormal event, remotely monitor the kitchen status via real-time streaming videos, and manually turn off the gas source using a smartphone as necessary. To accurately recognize the intensity of stove fire and detect abnormal fire intensity, deep learning based fire recognition methods using conventional and densely connected convolutional neural networks are developed to further improve the recognition accuracy of DeepSafe. In particular, the prototype consisting of an Android based APP and a Raspberry Pi based IoT device with the gas detector, image sensor, and 3-axis accelermeter are implemented to verify the feasibility and correctness of our DeepSafe system.