Volume 48 Issue 11
Nov.  2022
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SHE Xiaojiang, LIU Jiang, WANG Lanhao. Application status and prospect of AI video image analysis in intelligent coal preparation plant[J]. Journal of Mine Automation,2022,48(11):45-53, 109.  doi: 10.13272/j.issn.1671-251x.2022060092
Citation: SHE Xiaojiang, LIU Jiang, WANG Lanhao. Application status and prospect of AI video image analysis in intelligent coal preparation plant[J]. Journal of Mine Automation,2022,48(11):45-53, 109.  doi: 10.13272/j.issn.1671-251x.2022060092

Application status and prospect of AI video image analysis in intelligent coal preparation plant

doi: 10.13272/j.issn.1671-251x.2022060092
  • Received Date: 2022-06-25
  • Rev Recd Date: 2022-10-28
  • Available Online: 2022-08-30
  • Artificial intelligence (AI) video image analysis is an important part of intelligent coal preparation plant. It can realize the intelligent monitoring of important parameters of the equipment, environment, personnel and the whole process of coal preparation. The basic structure of the intelligent coal preparation plant is proposed. It is pointed out that the existing research mostly uses AI video image analysis technology to construct the safety monitoring system of coal preparation plant for personnel, machine, environment and management. The construction process of the intelligent video image monitoring system is proposed. In view of the two goals of safe and environment-friendly production and improving product quality in the intelligent construction of coal preparation plant, the application status of AI video image analysis technology in the intelligent coal preparation plant is introduced from six aspects. The aspects include foreign object detection, intelligent separation, equipment running state monitoring, coal particle size detection, personnel behavior monitoring and environmental safety detection. The intelligent application of AI video image analysis in coal preparation plant is proposed. It is pointed out that it is necessary to build a multi-level video monitoring system based on 5G communication, the Internet of Things, AI, intelligent control theory and coal preparation industry technology from the macro architecture. It is also necessary to optimize existing general intelligent video monitoring methods or algorithms from a micro perspective, and develop intelligent video image analysis technology suitable for the coal preparation plant environment. Machine vision and computer vision should be highly integrated with deep learning. The different advantages of machine vision and computer vision should be reasonably applied in different working conditions. It is suggested to establish a multi-level integrated monitoring system framework, and deploy and optimize the algorithm model within the framework. It is suggested to establish a diversified video image database, make full use of data characteristics of different image types, and develop targeted analysis algorithms. It is suggested to deeply study the distributed data stream and real-time AI video image analysis, build a real-time AI distributed system, reasonably schedule the video image analysis model, and improve the calculation efficiency and accuracy of the real-time model.

     

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  • [1]
    张家富. 选煤厂智能化技术和设备现状分析[J]. 煤炭加工与综合利用,2022(1):88-92. doi: 10.16200/j.cnki.11-2627/td.2022.01.017

    ZHANG Jiafu. Present situation analysis of intelligent technology and equipment in coal preparation plant[J]. Coal Processing & Comprehensive Utilization,2022(1):88-92. doi: 10.16200/j.cnki.11-2627/td.2022.01.017
    [2]
    匡亚莉. 智能化选煤厂建设的内涵与框架[J]. 选煤技术,2018,46(1):85-91. doi: 10.16447/j.cnki.cpt.2018.01.022

    KUANG Yali. The intension and framework for the construction of intelligent coal preparation plant[J]. Coal Preparation Technology,2018,46(1):85-91. doi: 10.16447/j.cnki.cpt.2018.01.022
    [3]
    赵亮,孙魁元,韩宝虎,等. 基于人工智能视频分析的选煤厂安全管理研究[J]. 中国安全科学学报,2021,31(增刊1):19-23. doi: 10.16265/j.cnki.issn1003-3033.2021.S1.004

    ZHAO Liang,SUN Kuiyuan,HAN Baohu,et al. Research on safety management of coal preparation plants based on artificial intelligence video analysis[J]. China Safety Science Journal,2021,31(S1):19-23. doi: 10.16265/j.cnki.issn1003-3033.2021.S1.004
    [4]
    杨景峰. 基于AI视频识别技术的井下规范操作监控系统设计[J]. 陕西煤炭,2021,40(1):4-8,46. doi: 10.3969/j.issn.1671-749X.2021.01.003

    YANG Jingfeng. Design of underground standard operation monitoring system based on AI video recognition technology[J]. Shaanxi Coal,2021,40(1):4-8,46. doi: 10.3969/j.issn.1671-749X.2021.01.003
    [5]
    WU Yaqin,CHEN Mengmeng,WANG Kai,et al. A dynamic information platform for underground coal mine safety based on Internet of things[J]. Safety Science,2019,113:9-18. doi: 10.1016/j.ssci.2018.11.003
    [6]
    CHEN Hao, ZI Xinli, ZHANG Qing, et al. Computer big data technology in Internet network communication video monitoring of coal preparation plant[C]. 2nd International Conference on Applied Physics and Computing(ICAPC), Ottawa, 2021: 1-6.
    [7]
    MA Long, CHENG Qing. Design and application of intelligent monitoring and identification system in coal mine[C]. 3rd International Conference on Green Energy and Sustainable Development, Shenyang, 2020: 1027-1031.
    [8]
    ZHANG Kanghui,WANG Weidong,LYU Ziqi,et al. Computer vision detection of foreign objects in coal processing using attention CNN[J]. Engineering Applications of Artificial Intelligence,2021,102:116-128.
    [9]
    ZHAO Xiaohu, LI Xiao, YIN Liangfei, et al. Foreign body recognition for coal mine conveyor based on improved PCANet[C]. 11th International Conference on Wireless Communications and Signal Processing (WCSP), Xi'an, 2019: 1-6.
    [10]
    WANG Yuanbin,WANG Yujiang,DANG Langfei. Video detection of foreign objects on the surface of belt conveyor underground coal mine based on improved SSD[J]. Journal of Ambient Intelligence and Humanized Computing,2020:1-10. DOI: 10.1007/s12652-020-02495-w.
    [11]
    高小强. 智能巡检机器人视频监测皮带异物自动识别报警技术研究[J]. 电子技术与软件工程,2016(11):158-160.

    GAO Xiaoqiang. Research on automatic recognition and alarm technology of belt foreign matters monitored by intelligent inspection robot[J]. Electronic Technology & Software Engineering,2016(11):158-160.
    [12]
    郭亮. 基于视频图像处理的煤与矸石分选方法的研究[D]. 青岛: 山东科技大学, 2014.

    GUO Liang. Study of the coal and gangue sorting method based on the video image processing[D]. Qingdao: Shandong University of Science and Technology, 2014.
    [13]
    丁泽海,薛斌,窦东阳. 图像处理在煤矸石分选系统中的应用[J]. 煤矿机械,2017,38(3):173-175.

    DING Zehai,XUE Bin,DOU Dongyang. Application of image processing in coal and gangue separation system[J]. Coal Mine Machinery,2017,38(3):173-175.
    [14]
    吴开兴,宋剑. 基于灰度共生矩阵的煤与矸石自动识别研究[J]. 煤炭工程,2016,48(2):98-101.

    WU Kaixing,SONG Jian. Automatic coal-gangue identification based on gray level co-occurrence matrix[J]. Coal Engineering,2016,48(2):98-101.
    [15]
    张勇. 基于视频处理的煤矸石识别研究[D]. 徐州: 中国矿业大学, 2018.

    ZHANG Yong. Research on gangue identification based on video processing[D]. Xuzhou: China University of Mining and Technology, 2018.
    [16]
    徐志强,吕子奇,王卫东,等. 煤矸智能分选的机器视觉识别方法与优化[J]. 煤炭学报,2020,45(6):2207-2216. doi: 10.13225/j.cnki.jccs.zn20.0307

    XU Zhiqiang,LYU Ziqi,WANG Weidong,et al. Machine vision recognition method and optimization for intelligent separation of coal and gangue[J]. Journal of China Coal Society,2020,45(6):2207-2216. doi: 10.13225/j.cnki.jccs.zn20.0307
    [17]
    LI Dongjun,ZHANG Zhenxin,XU Zhihua,et al. An image-based hierarchical deep learning framework for coal and gangue detection[J]. IEEE Access,2019:7. DOI: 10.1109/access.2019.2961075.
    [18]
    MORAR S H,HARRIS M C,BRADSHAW D J. The use of machine vision to predict flotation performance[J]. Minerals Engineering,2012(36/37/38):31-36.
    [19]
    MASSINAEI M,JAHEDSARAVANI A,MOHSENI H. Recognition of process conditions of a coal column flotation circuit using computer vision and machine learning[J]. International Journal of Coal Preparation and Utilization,2022,42(7):2204-2218. doi: 10.1080/19392699.2020.1823843
    [20]
    唐朝晖,刘金平,陈青,等. 基于预测模型的浮选过程pH值控制[J]. 控制理论与应用,2013,30(7):885-890. doi: 10.7641/CTA.2013.12042

    TANG Zhaohui,LIU Jinping,CHEN Qing,et al. pH control in flotation process based on prediction model[J]. Control Theory & Applications,2013,30(7):885-890. doi: 10.7641/CTA.2013.12042
    [21]
    阳春华,任会峰,桂卫华,等. 基于机器视觉的矿物浮选pH软测量方法[J]. 计算机工程与应用,2011,47(1):228-230,248. doi: 10.3778/j.issn.1002-8331.2011.01.065

    YANG Chunhua,REN Huifeng,GUI Weihua,et al. Machine-vision-based soft sensor of pH for flotation process[J]. Computer Engineering and Applications,2011,47(1):228-230,248. doi: 10.3778/j.issn.1002-8331.2011.01.065
    [22]
    ZHU Aichun, HUA Gang, WANG Yongxing. The research on the detection method of belt deviation by video in coal mine[C]. International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), Jilin, 2011: 430-433.
    [23]
    滕悦,徐少川,张庆东. 基于图像处理技术的皮带跑偏监测系统设计[J]. 烧结球团,2020,45(2):10-14. doi: 10.13403/j.sjqt.2020.02.018

    TENG Yue,XU Shaochuan,ZHANG Qingdong. Design of monitoring system for belt deviation based on image processing technology[J]. Sintering and Pelletizing,2020,45(2):10-14. doi: 10.13403/j.sjqt.2020.02.018
    [24]
    田勇. 机器视觉技术在选煤厂运输机溜槽堵塞检测中的应用[J]. 山西能源学院学报,2021,34(4):5-6,9. doi: 10.3969/j.issn.1008-8881.2021.04.002

    TIAN Yong. Application of machine vision technology in detection of transport chute blockage in coal preparation plant[J]. Journal of Shanxi Institute of Energy,2021,34(4):5-6,9. doi: 10.3969/j.issn.1008-8881.2021.04.002
    [25]
    GB/T17608—2006 煤炭产品品种和等级划分[S].

    GB/T17608—2006 Coal product variety and grade division[S].
    [26]
    张雷, 孙颖, 田志辉. 基于机器视觉的物料粒度在线检测方法: 201811478128.8[P]. 2019-04-09.

    ZHANG Lei, SUN Ying, TIAN Zhihui. Online detection method of material granularity based on machine vision: 201811478128.8[P]. 2019-04-09.
    [27]
    郭福彧. 基于机器视觉的细碎矿石粒度分布在线检测技术研究[D]. 沈阳: 东北大学, 2015.

    GUO Fuyu. Research on the technology of the fine crushing ore particle size distribution on-line detection based on machine vision[D]. Shenyang: Northeastern University, 2015.
    [28]
    董珂. 基于机器视觉的矿石粒度检测技术研究[D]. 北京: 北京工业大学, 2013.

    DONG Ke. Research on ore granularity detection technology based on machine vision[D]. Beijing: Beijing University of Technology, 2013.
    [29]
    张宗华. 选煤厂人员智能视频监控系统设计[J]. 工矿自动化,2013,39(4):76-79. doi: 10.7526/j.issn.1671-251X.2013.04.020

    ZHANG Zonghua. Design of intelligent video monitoring system of personnel of coal preparation plant[J]. Industry and Mine Automation,2013,39(4):76-79. doi: 10.7526/j.issn.1671-251X.2013.04.020
    [30]
    朱煜,赵江坤,王逸宁,等. 基于深度学习的人体行为识别算法综述[J]. 自动化学报,2016,42(6):848-857. doi: 10.16383/j.aas.2016.c150710

    ZHU Yu,ZHAO Jiangkun,WANG Yining,et al. A review of human action recognition based on deep learning[J]. Acta Automatica Sinica,2016,42(6):848-857. doi: 10.16383/j.aas.2016.c150710
    [31]
    刘忠育. 基于深度学习的矿工不安全行为识别方法研究[D]. 徐州: 中国矿业大学, 2021.

    LIU Zhongyu. Research on recognition methods of miners' unsafe behavior based on deep learning[D]. Xuzhou: China University of Mining and Technology, 2021.
    [32]
    冯小琴. 多场景视频智能处理系统及调度管理算法研究[D]. 北京: 北京工业大学, 2019.

    FENG Xiaoqin. Research on multi-scene video intelligent processing system and scheduling management algorithm[D]. Beijing: Beijing University of Technology, 2019.
    [33]
    周晨晖. 基于深度学习的煤矿复杂场景人员检测与统计分析方法研究[D]. 徐州: 中国矿业大学, 2019.

    ZHOU Chenhui. Research on personnel detection and statistical analysis in coal mine complex scenes based on deep learning[D]. Xuzhou: China University of Mining and Technology, 2019.
    [34]
    张翼翔,林松,李雪. 基于CenterNet-GhostNet的选煤厂危险区域人员检测[J]. 工矿自动化,2022,48(4):66-71. doi: 10.13272/j.issn.1671-251x.2021080058

    ZHANG Yixiang,LIN Song,LI Xue. Personnel detection in dangerous area of coal preparation plant based on CenterNet-GhostNet[J]. Journal of Mine Automation,2022,48(4):66-71. doi: 10.13272/j.issn.1671-251x.2021080058
    [35]
    LI Guohui,WU Jieping,LUO Zhiwen,et al. Vision-based measurement of dust concentration by image transmission[J]. IEEE Transactions on Instrumentation and Measurement,2019,68(10):3942-3949. doi: 10.1109/TIM.2018.2883999
    [36]
    WANG Zheng,ZHENG Xu,LI Dongyan,et al. A VGGNet-like approach for classifying and segmenting coal dust particles with overlapping regions[J]. Computers in Industry,2021,132. DOI: 10.1016/J.COMPIND.2021.103506.
    [37]
    宋敬海. 基于嵌入式系统和机器视觉的火灾检测系统研究[D]. 镇江: 江苏科技大学, 2008.

    SONG Jinghai. Research on fire detection system based on embedded system and machine vision[D]. Zhenjiang: Jiangsu University of Science and Technology, 2008.
    [38]
    CUI Haoyang, XU Yongpeng, ZENG Jundong, et al. The methods in infrared thermal imaging diagnosis technology of power equipment[C]. IEEE 4th International Conference on Electronics Information and Emergency Communication, Beijing, 2013: 246-251.
    [39]
    JADIN M S, GHAZALI K H. Gas leakage detection using thermal imaging technique[C]. The 16th International Conference on Computer Modelling and Simulation, Cambridge, 2014: 302-306.
    [40]
    NARKHEDE P,WALAMBE R,MANDAOKAR S,et al. Gas detection and identification using multimodal artificial intelligence based sensor fusion[J]. Applied System Innovation,2021,4(1):1-14.
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