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Fire Detection Algorithm Combining Adaptive Gaussian Mixture Model and ResDN
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Wen-biao WANG, Qi-heng SHI, You-wei HAO
Science Technology and Engineering | 2025, 25(4) : 1580 - 1586
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Science Technology and Engineering | 2025, 25(4): 1580-1586
• Papers·Automation and Computational Technology •
Fire Detection Algorithm Combining Adaptive Gaussian Mixture Model and ResDN
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Wen-biao WANG, Qi-heng SHI, You-wei HAO
Affiliations
  • School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Published: 2025-02-08 doi: 10.12404/j.issn.1671-1815.2309705
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A lightweight and efficient two-stage video flame detection algorithm was designed to address issues of high false positive rates, poor adaptability, and low efficiency in complex scenes. In the first stage, an improved adaptive Gaussian mixture model (AGMM) was employed for rapid background modeling of video image sequences. Suspicious candidate regions were extracted from the sequences by leveraging the flickering and surging characteristics of flames. In the second stage, a residual deep normalization and convolutional neural network (ResDN) was used to discriminate these suspicious candidate regions. A simplified residual block was introduced to replace the original convolutional layers for a lightweight design, enabling accurate flame detection and localization. Compared with traditional classification algorithms, the proposed two-stage video flame detection algorithm effectively overcomes environmental interference in complex scenes, rapidly and accurately identifies flames, and demonstrates higher detection rates and adaptability.

flame detection  /  adaptive Gaussian mixture model(AGMM)  /  residual deep normalization and convolutional neural network(ResDN)  /  machine vision  /  deep learning
Wen-biao WANG, Qi-heng SHI, You-wei HAO. Fire Detection Algorithm Combining Adaptive Gaussian Mixture Model and ResDN[J]. Science Technology and Engineering, 2025 , 25 (4) : 1580 -1586 . DOI: 10.12404/j.issn.1671-1815.2309705
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Year 2025 volume 25 Issue 4
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Article Info
doi: 10.12404/j.issn.1671-1815.2309705
  • Receive Date：2023-12-08
  • Online Date：2025-07-29
  • Published：2025-02-08
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  • Received：2023-12-08
  • Revised：2024-11-15
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    School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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