Mine exogenous fire monitoring method using the fusion of infrared visual features

LI Xiaoyu; FAN Weiqiang; LIU Yi; HUO Yuehua

doi:10.19606/j.cnki.jmst.2024930

Volume 10 Issue 1

Feb. 2025

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Journal of Mining Science and Technology > 2025 > 10(1): 116-124. > DOI: 10.19606/j.cnki.jmst.2024930

LI Xiaoyu, FAN Weiqiang, LIU Yi, et al. Mine exogenous fire monitoring method using the fusion of infrared visual features[J]. Journal of Mining Science and Technology, 2025, 10(1): 116-124. DOI: 10.19606/j.cnki.jmst.2024930

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Mine exogenous fire monitoring method using the fusion of infrared visual features

LI Xiaoyu^{1, 2,},
FAN Weiqiang^{1, 2, ,},
LIU Yi³,
HUO Yuehua^{3, 4}

1.
School of Electronic Information Engineering, Inner Mongolia University, Hohhot Inner Mongolia 010021, China
2.
Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing, Hohhot Inner Mongolia 010021, China
3.
School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China
4.
Network and Information Center, China University of Mining and Technology-Beijing, Beijing 100083, China

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Received Date: August 08, 2024
Revised Date: September 09, 2024

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Abstract

Abstract

In order to solve the problems of high false positive and false negative rates of external fire monitoring in complex mine environments, a monitoring algorithm using infrared visual feature fusion was proposed. Firstly, the Local Contrast Measure (LCM) model for infrared small target detection was improved to enhance the saliency of early-stage fire targets, thereby segmenting out suspected fire areas. Then, by analyzing the visual features of exogenous fires and major interfering heat sources in thermal infrared image sequences under different surveillance scenarios, the salient features of fires with strong anti-interference ability were preferred. Next, fire salient feature extraction methods and similarity estimation strategies were optimized to obtain the main visual features of suspected fire areas in the thermal infrared image sequences and construct a fire feature vector. Finally, by establishing a feature vector set and constructing a mine exogenous fire detection model using Support Vector Machine (SVM), the proposed algorithm was experimentally validated. The results show that the proposed algorithm realizes exogenous fire monitoring in different scenarios, as well as in remote and early stages, with accuracy and detection rates of 96.93 % and 96.24 %, respectively, and a false detection rate of 2.56 %. Compared to the described comparison algorithms, the proposed method has better improvements in the accuracy, false alarm rate, and leakage alarm rate of fire monitoring.
- mine exogenous fire,
- infrared visual features,
- local contrast measure (LCM) model,
- eigenvector,
- support vector machine (SVM)

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References

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