A Machine Learning and Embedded Approach to Detect Fire, Identify the Cause of it and Extinguish Using the Best Possible Extinguisher

Abstract

Chemical fires are a major cause of devastating situations and fatalities in Bangladesh, where inadequate knowledge of extinguishing such fires has led to numerous deaths, including those of firefighters. To address this issue, we propose a machine learning and embedded systems approach to design a dynamic and portable prototype that can remotely monitor the fire incident and determine its cause. The prototype uses a dashboard camera to stream video and gas sensors to detect the chemical gas information in the environment to identify and extinguish the fire. The embedded approach clarifies the cause of the fire and suggests the most suitable extinguisher to the user. The device is economical, portable, user-friendly, and marketable in Bangladesh. The system produces satisfactory results with high accuracy in fire detection and maintains its objective of being low-cost and user-friendly. The device can be remotely controlled using a mobile phone and web-based application. The system's machine learning approach uses appropriate datasets and algorithms (YOLOV5), while the embedded approach employs suitable sensors and a microcontroller (ESP32). The system's accuracy has been shown to be perfect.