Enhancing Fire and Smoke Detection Using Deep Learning Techniques

Abstract

This exploration focuses on the effective detection of fire and smoke in various environments, both indoors and outdoors, through the application of real-time object detection and image-processing deep learning algorithms. Capturing the essence of our investigation study delves into advancements and applications of real-time deep learning algorithms in safeguarding lives and property from fire-related risks. There are several factors that affect the outbreak of fire, which then becomes uncontrollable just with human efforts. Smoke is the prelude of many fire incidents. Apart from that, smoke emitted by some electronic or chemical components can possess harmful gases which are very much injurious to human health and must be prevented. This paper’s aim is to provide a comprehensive review of existing research in this field, highlighting significant milestones achieved in the path of our exploration. The analysis will concentrate on evaluating the effectiveness of various strategies implemented to date and their practical applications in real-world settings. The in-depth examination will highlight why image-based detection can be more efficient than currently employed sensors. This work illustrates the critical importance of leveraging cutting-edge technology to enhance fire and smoke detection, ultimately contributing to the safety and well-being of individuals and the protection of property.

Keywords

• fire detection
• smoke detection
• image segmentation
• deep learning
• strategy analysis