Flame detection algorithm based on adaptive Gaussian mixture model and DNCNN

Abstract

In order to address the challenges of high false alarm rate, slow modeling speed, and poor real-time performance in flame detection algorithms under complex scenes, a lightweight and efficient two-stage video flame detection algorithm was designed.In the first stage, an Adaptive Gaussian Mixture Model (AGMM) is utilized to rapidly build the background model of the video image sequence and extract suspicious candidate regions from the sequence.In the second stage, a Deep Normalization and Convolutional Neural Network (DNCNN) is employed to discriminate the suspicious candidate regions, achieving accurate localization of flames.Compared to traditional classification algorithms, the proposed two-stage video flame detection algorithm effectively overcomes environmental disturbances in complex scenes, accurately and rapidly identifies flames, and demonstrates higher detection rates and adaptability.