Volume 48 Issue 12
Dec.  2022
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ZHAO Duan, LI Tao, DONG Yanqiang, et al. Coal mine external fire detection method based on edge intelligence[J]. Journal of Mine Automation，2022，48(12)：108-115. DOI: 10.13272/j.issn.1671-251x.2022080046
Citation: ZHAO Duan, LI Tao, DONG Yanqiang, et al. Coal mine external fire detection method based on edge intelligence[J]. Journal of Mine Automation，2022，48(12)：108-115. DOI: 10.13272/j.issn.1671-251x.2022080046
shu

Coal mine external fire detection method based on edge intelligence

  • 1.

    National and Local Joint Engineering Laboratory of Mining Internet Application Technology, China University of Mining and Technology, Xuzhou 221116, China

  • 2.

    School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

  • 3.

    Inner Mongolia Baiyinhua Coal and Electricity Co., Ltd., State Power Investment Group, Xilin Gol 026200, China

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  • Received Date: August 15, 2022
  • Revised Date: December 09, 2022
  • Available Online: December 04, 2022
    Graphical Abstract
    • Abstract
  • The detection of external fire in coal mines and the reliable identification of initial fire are of great significance for improving the level of coal mine fire detection. It is also an important direction of intelligent mine construction in the future. In order to improve the speed, precision and real-time of coal mine external fire detection, a coal mine external fire detection method based on edge intelligence is proposed. The feature scale of the backbone network of the YOLOv5s model is improved. The model can fully learn the shallow features and improve the small target detection performance. At the same time, an adaptive attention module is added on the basis of the original feature pyramid network (FPN) to improve the detection precision of the model. There are problems of image detection error and missed detection caused by poor light conditions, more dust and camera shooting angle in the underground mine. In order to solve the above problems, the YOLOv5s-as model is constructed by using multi-sensor auxiliary detection and weighting fusion identification of video detection information and multi-sensor detection information through dynamic weighting algorithm. The YOLOv5s-as model is transplanted to the intelligent edge processor, and lightweight processing is carried out to realize the deployment of edge intelligent devices. The experimental results show that the reasoning time of the YOLOv5s-as model is slightly longer than that of the YOLOv5s-a model without sensor information fusion reasoning, but mean value of average precision when the intersection over union is 0.5 (mAP@0.5) is increased by 7.24%. Compared with the YOLOv5s model before transplantation, the mAP@0.5 of the YOLOv5s-as model transplanted to the intelligent edge processor and subjected to lightweight processing increased by 15.04%. For small target fire sources, SSD 300, SSD 512 and YOLOv5s models cannot identify them. The YOLOv5s-a and YOLOv5s-as models can detect small target fire sources with good adaptability. When using the edge processing method, the response period of YOLOv5s as model is 238 ms, which is 38.66% shorter than the centralized processing method.
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  • [1]
    孙继平,钱晓红. 2004—2015年全国煤矿事故分析[J]. 工矿自动化,2016,42(11):1-5. DOI: 10.13272/j.issn.1671-251x.2016.11.001

    SUN Jiping,QIAN Xiaohong. Analysis of coal mine accidents in China during 2004-2015[J]. Industry and Mine Automation,2016,42(11):1-5. DOI: 10.13272/j.issn.1671-251x.2016.11.001
    [2]
    宁小亮. 2013—2018年全国煤矿事故规律分析及对策研究[J]. 工矿自动化,2020,46(7):34-41. DOI: 10.13272/j.issn.1671-251x.17610

    NING Xiaoliang. Law analysis and counter measures research of coal mine accidents in China from 2013 to 2018[J]. Industry and Mine Automation,2020,46(7):34-41. DOI: 10.13272/j.issn.1671-251x.17610
    [3]
    孟远,谢东海,苏波,等. 2010年—2019年全国煤矿生产安全事故统计与现状分析[J]. 矿业工程研究,2020,35(4):27-33. DOI: 10.13582/j.cnki.1674-5876.2020.04.005

    MENG Yuan,XIE Donghai,SU Bo,et al. Statistics and analysis of coal mine production safety accidents in China from 2010 to 2019[J]. Mineral Engineering Research,2020,35(4):27-33. DOI: 10.13582/j.cnki.1674-5876.2020.04.005
    [4]
    何勇军,易欣,王伟峰,等. 煤矿井下电气火灾智能监控与灭火技术综述[J]. 煤矿安全,2022,53(9):55-64. DOI: 10.13347/j.cnki.mkaq.2022.09.008

    HE Yongjun,YI Xin,WANG Weifeng,et al. Review of intelligent monitoring and extinguishing technology of electrical fire in coal mine[J]. Safety in Coal Mines,2022,53(9):55-64. DOI: 10.13347/j.cnki.mkaq.2022.09.008
    [5]
    李国伟. 石丘煤矿外因火灾防治措施分析[J]. 能源与节能,2022(6):111-113. DOI: 10.3969/j.issn.2095-0802.2022.06.035

    LI Guowei. Prevention and control measures of exogenous fire in Shiqiu Coal Mine[J]. Energy and Energy Conservation,2022(6):111-113. DOI: 10.3969/j.issn.2095-0802.2022.06.035
    [6]
    王慧,宋宇宁. D−S证据理论在火灾检测中的应用[J]. 中国安全科学学报,2016,26(5):19-23.

    WANG Hui,SONG Yuning. Application of D-S evidence theory in fire detection[J]. China Safety Science Journal,2016,26(5):19-23.
    [7]
    黄圆明,徐泽. 基于DSP的多传感器信息融合的厨房火灾检测系统[J]. 科学技术创新,2020(11):51-53. DOI: 10.3969/j.issn.1673-1328.2020.11.028

    HUANG Yuanming,XU Ze. Kitchen fire detection system based on DSP multi-sensor information fusion[J]. Scientific and Technological Innovation,2020(11):51-53. DOI: 10.3969/j.issn.1673-1328.2020.11.028
    [8]
    段锁林,杨可,毛丹,等. 基于模糊证据理论算法在火灾检测中的应用[J]. 计算机工程与应用,2017,53(5):231-235. DOI: 10.3778/j.issn.1002-8331.1507-0231

    DUAN Suolin,YANG Ke,MAO Dan,et al. Fuzzy evidence theory-based algorithm in application of fire detection[J]. Computer Engineering and Applications,2017,53(5):231-235. DOI: 10.3778/j.issn.1002-8331.1507-0231
    [9]
    兰琪,贾敏智. 基于图像型的矿井火灾探测方法研究[J]. 煤炭技术,2016,35(4):187-189. DOI: 10.13301/j.cnki.ct.2016.04.076

    LAN Qi,JIA Minzhi. Research on fire detection based on image in coal mine[J]. Coal Technology,2016,35(4):187-189. DOI: 10.13301/j.cnki.ct.2016.04.076
    [10]
    孙继平,孙雁宇,范伟强. 基于可见光和红外图像的矿井外因火灾识别方法[J]. 工矿自动化,2019,45(5):1-5,21. DOI: 10.13272/j.issn.1671-251x.17435

    SUN Jiping,SUN Yanyu,FAN Weiqiang. Mine exogenous fire identification method based on visible light and infrared image[J]. Industry and Mine Automation,2019,45(5):1-5,21. DOI: 10.13272/j.issn.1671-251x.17435
    [11]
    孙继平,崔佳伟. 矿井外因火灾感知方法[J]. 工矿自动化,2021,47(4):1-5,38. DOI: 10.13272/j.issn.1671-251x.17760

    SUN Jiping,CUI Jiawei. Mine external fire sensing method[J]. Industry and Mine Automation,2021,47(4):1-5,38. DOI: 10.13272/j.issn.1671-251x.17760
    [12]
    何凯,冯旭,高圣楠,等. 基于多尺度特征融合与反复注意力机制的细粒度图像分类算法[J]. 天津大学学报(自然科学与工程技术版),2020,53(10):1077-1085.

    HE Kai,FENG Xu,GAO Shengnan,et al. Fine-grained image classification algorithm using multi-scale feature fusion and re-attention mechanism[J]. Journal of Tianjin University(Science and Technology),2020,53(10):1077-1085.
    [13]
    HUANG Chenchen, CHEN Siqi, XU Longtao. Object detection based on multi-source information fusion in different traffic scenes[C]. 12th International Conference on Advanced Computational Intelligence, Dali, 2020: 213-217.
    [14]
    傅天驹,郑嫦娥,田野,等. 复杂背景下基于深度卷积神经网络的森林火灾识别[J]. 计算机与现代化,2016(3):52-57. DOI: 10.3969/j.issn.1006-2475.2016.03.011

    FU Tianju,ZHENG Chang'e,TIAN Ye,et al. Forest fire recognition based on deep convolutional neural network under complex background[J]. Computer and Modernization,2016(3):52-57. DOI: 10.3969/j.issn.1006-2475.2016.03.011
    [15]
    喻丽春,刘金清. 基于改进Mask R−CNN的火焰图像识别算法[J]. 计算机工程与应用,2020,56(21):194-198. DOI: 10.3778/j.issn.1002-8331.2006-0194

    YU Lichun,LIU Jinqing. Fire image recognition algorithm based on improved Mask R-CNN[J]. Computer Engineering and Applications,2020,56(21):194-198. DOI: 10.3778/j.issn.1002-8331.2006-0194
    [16]
    侯易呈,王慧琴,王可. 改进的多尺度火焰检测方法[J]. 液晶与显示,2021,36(5):751-759. DOI: 10.37188/CJLCD.2020-0221

    HOU Yicheng,WANG Huiqin,WANG Ke. Improved multi-scale flame detection method[J]. Chinese Journal of Liquid Crystals and Displays,2021,36(5):751-759. DOI: 10.37188/CJLCD.2020-0221
    [17]
    REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[EB/OL]. [2022-07-21]. https://arxiv.org/abs/1506.02640.
    [18]
    WANG C-Y, LIAO H-Y M, WU Y-H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, 2020: 390-391.
    [19]
    LIU Shu, QI Lu, QIN Haifang, et al. Path aggregation network for instance segmentation[EB/OL]. [2022-07-20]. https://arxiv.org/abs/1803.01534.
    [20]
    柳小军,鲍鸿. 基于ARM9多传感器数据融合火灾报警系统的实现[J]. 工业控制计算机,2009,22(3):52-53. DOI: 10.3969/j.issn.1001-182X.2009.03.028

    LIU Xiaojun,BAO Hong. Implementation of fire alarm system based on ARM9 multi-sensor data fusion[J]. Industrial Control Computer,2009,22(3):52-53. DOI: 10.3969/j.issn.1001-182X.2009.03.028
    [21]
    HOWARD A G, ZHU Menglong, CHEN Bo, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. [2022-07-20]. https://arxiv.org/abs/1704.04861.
    [22]
    LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]. European Conference on Computer Vision, 2016: 21-37.
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