Mine external fire monitoring method using the fusion of visible visual features

Fan Weiqiang; Li Xiaoyu; Liu Yi; Weng Zhi

doi:10.19606/j.cnki.jmst.2023.04.009

Volume 8 Issue 4

Aug. 2023

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Article Navigation > Journal of Mining Science and Technology > 2023 > 8(4): 529-537

Fan Weiqiang, Li Xiaoyu, Liu Yi, Weng Zhi. Mine external fire monitoring method using the fusion of visible visual features[J]. Journal of Mining Science and Technology, 2023, 8(4): 529-537. doi: 10.19606/j.cnki.jmst.2023.04.009

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Mine external fire monitoring method using the fusion of visible visual features

doi: 10.19606/j.cnki.jmst.2023.04.009

Fan Weiqiang^{1, 2
,},
Li Xiaoyu^{1, 2
,
,},
Liu Yi²,
Weng Zhi¹

1.
School of Electronic Information Engineering, Inner Mongolia University, Hohhot Inner Mongolia 010021, China
2.
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

Received Date: 2022-11-02
Rev Recd Date: 2022-12-19
Publish Date: 2023-08-31

Abstract

Abstract

In order to overcome the problems of poor real-time performance, high false alarm rate and underreport alarm rate of mine external fire monitoring, a method of fire monitoring using the fusion of visible visual features is proposed.Firstly, the visual features corresponding to the video images of fire sources in different monitoring environments are analyzed, and the extraction methods of fire source texture, sharp corners, similarity coefficient and flicker frequency are designed.Then, an improved seed region growth algorithm is used to segment the suspected fire area, and different feature extraction methods are used to calculate the dynamic and static characteristics of the suspected fire area.Secondly, the extracted dynamic and static features are used to construct fire feature vectors.Finally, a fire monitoring model using BP neural network is constructed, and monitoring model is verified.The results show that the proposed fire monitoring method can effectively detect mine external fire in different scenes and distances.The correct rate and detection rate are 98.60% and 99.06%, respectively, the false detection rate is as low as 2.00%.It has strong anti-interference ability and robustness.
- external fire,
- fire monitoring,
- static features,
- dynamic features,
- feature fusion,
- BP neural networks

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References(24)

References

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Mine external fire monitoring method using the fusion of visible visual features

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