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Fire detection technology in open-pit mines based on infrared images and target detection
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Haicheng YU, Yu TIAN, Qingjian LI, Xinpeng LI, Guoqing XUE, Yuhua ZHANG
China Safety Science Journal | 2024, 34(S1) : 212 - 218
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China Safety Science Journal | 2024, 34(S1): 212-218
Safety engineering technology
Fire detection technology in open-pit mines based on infrared images and target detection
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Haicheng YU, Yu TIAN, Qingjian LI, Xinpeng LI, Guoqing XUE, Yuhua ZHANG
Affiliations
  • Open-pit Coal Mine of Baori Shiller Energy Co.,Ltd.,National Energy Group,Hulunbuir Inner Mongolia 021008,China
Published: 2024-06-30 doi: 10.16265/j.cnki.issn1003-3033.2024.S1.0031
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In order to detect the spontaneous combustion of coal gangue in open-pit mines in time and avoid greater losses caused by the spread of fire,the technology of spontaneous combustion and fire detection in open-pit mines based on infrared thermal imaging and deep learning was studied. As a result,the problem that a single detection method cannot achieve all-round spontaneous combustion and fire detection of coal gangue in large open-pit mines was solved. First,it was proposed to realize spontaneous combustion and fire detection in different areas through infrared thermal imaging and fire image target recognition,and a spontaneous combustion recognition of coal seam and early warning system architecture of open-pit mines was built. Then,thermal imaging equipment fixed on the ground of the open-pit mine was used to capture infrared images of the coal gangue area where no fire has occurred in real time and monitor the temperature changes of the coal gangue. Finally,the drone with an airborne camera was used to take photos of coal seams,waste piles,and surrounding environments,and then a fire detection model was built through the YOLOv8 target detection algorithm based on deep learning,so as to realize the detection of image targets of flames and smoke,thereby completing fire detection and early warning. The results show that the joint detection technology based on thermal infrared images and flame and smoke image recognition has an average detection accuracy of 70.5% for flames and an average recognition accuracy of 74% for smoke,which can meet the detection needs of spontaneous combustion and fire in open-pit mines.

infrared thermal imaging  /  open-pit mine  /  fire detection  /  spontaneous combustion of coal seam  /  deep learning
Haicheng YU, Yu TIAN, Qingjian LI, Xinpeng LI, Guoqing XUE, Yuhua ZHANG. Fire detection technology in open-pit mines based on infrared images and target detection[J]. China Safety Science Journal, 2024 , 34 (S1) : 212 -218 . DOI: 10.16265/j.cnki.issn1003-3033.2024.S1.0031
Year 2024 volume 34 Issue S1
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2024.S1.0031
  • Receive Date:2024-03-12
  • Online Date:2025-07-09
  • Published:2024-06-30
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  • Received:2024-03-12
  • Revised:2024-05-15
Affiliations
    Open-pit Coal Mine of Baori Shiller Energy Co.,Ltd.,National Energy Group,Hulunbuir Inner Mongolia 021008,China
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https://castjournals.cast.org.cn/joweb/zgaqkxxb/EN/10.16265/j.cnki.issn1003-3033.2024.S1.0031
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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