Flame detection algorithm based on adaptive Gaussian mixture model and DNCNN

Qiheng Shi; Wenbiao Wang; Youwei Hao

doi:10.1117/12.3029275

22 May 2024 Flame detection algorithm based on adaptive Gaussian mixture model and DNCNN

Qiheng Shi, Wenbiao Wang, Youwei Hao

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 1317605 (2024) https://doi.org/10.1117/12.3029275
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qiheng Shi, Wenbiao Wang, and Youwei Hao "Flame detection algorithm based on adaptive Gaussian mixture model and DNCNN", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 1317605 (22 May 2024); https://doi.org/10.1117/12.3029275

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KEYWORDS

Detection and tracking algorithms

Flame

Flame detectors

Modeling

Video

Education and training

Evolutionary algorithms

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