Recent Advances in Flame Detection Using Convolutional Neural Networks: A Review

Abstract

Flame detection plays a critical role in fire prevention, with early detection essential for minimizing damage and ensuring safety. Convolutional Neural Networks (CNNs) have emerged as a powerful tool for improving flame detection accuracy, speed, and reliability. This paper reviews recent advancements in CNN-based flame detection, highlighting methods that have enhanced detection accuracy, reduced false positives, and improved dataset quality. Practical applications in areas such as forest fire monitoring, building safety, and industrial fire prevention are discussed. The review aims to encourage further research into innovative CNN-based flame detection methods to develop more efficient and effective fire detection systems.