Enhancing Fire Detection through CNN and Transfer Learning: A Comprehensive Research Study

Abstract

Fire outbreaks pose significant risks to life, property, and the environment. Swift and accurate fire detection is crucial to minimize the potential damage. In recent years, advancements in computer vision techniques, particularly Convolutional Neural Networks (CNNs) and Transfer Learning, have revolutionized the field of fire detection. This article delves and introduce the comprehensive research study has taken place by the first three authors under guideline of their instructor into the integration of CNN and Transfer Learning in fire detection, highlighting their effectiveness and potential impact on enhancing fire safety measures. Fire detection plays a critical role in safeguarding lives and property. Traditional fire detection systems often rely on manual intervention and have limitations in terms of accuracy, response time, and adaptability. The advent of machine learning (ML) techniques has revolutionized fire detection by enabling the development of intelligent systems that can identify and respond to fires in real-time. This paper presents a comprehensive review of the advancements in ML-driven fire detection techniques, discusses their benefits and challenges, and outlines potential future directions for research and development.and outlines potential future directions for research and development.s of splicing for both air and water mood. The updated 92Z2 showed good result for both method