Remote supervision and management method for coal mine gas extraction drilling site based on AI video analysis
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摘要: 传统的煤矿瓦斯抽采钻场视频监控系统在钻孔施工及退钻杆期间，只具有监测和存储功能，重要的过程参数或信息只能由监测人员通过视频录像查看，存在记录施工信息易出错、钻场管理人员难以连续监控现场视频等问题。针对上述问题，提出了一种基于AI视频分析的煤矿瓦斯抽采钻场远程监督管理方法。该方法包括信息牌检测、OCR识别、退杆分析3种算法。信息牌检测用于检测当前施工环节，OCR识别用于识别信息牌上打钻流程与施工信息，退杆分析用于分析收孔阶段的退杆数，从而实现打钻作业的全过程分析与管控。在接收并开始打钻任务后，启用信息牌检测与OCR识别服务，根据依次识别到的开孔、收孔、封孔流程与施工参数，自动保存施工信息。当识别出开始收孔，启用退杆分析服务；当识别出结束收孔，停止退杆分析服务。实验结果表明：信息牌检测算法的识别准确率为96%。PaddleOCR识别算法平均用时17.51 ms，较EasyOCR、ChineseOCR识别算法分别降低了25.25，4.34 ms；PaddleOCR识别算法的准确率较其他2种识别算法分别提高了5.75%，2.29%，召回率较其他2种识别算法分别提高了9.77%，2.36%。退杆分析算法能够有效识别现场退杆数，准确率约为95%。Abstract: The traditional video monitoring system for coal mine gas extraction drilling site only has monitoring and storage functions during drilling construction and drill pipe withdrawal. Important process parameters or information can only be viewed by monitoring personnel through video recordings, which poses problems such as construction information being prone to errors and difficulty for drilling site management personnel to continuously monitor on-site videos. It order to solve the above problems, A remote supervision and management method for coal mine gas extraction drilling sites based on AI video analysis has been proposed. This method includes three algorithms: information board detection, OCR recognition, and drill pipe withdrawal analysis. Information board detection is used to detect the current construction phase. PaddleOCR recognition is used to recognize the drilling process and construction information on the information board. The drill pipe withdrawal analysis is used to analyze the number of drill pipes withdrawn during the closing drilling phase, thereby achieving the full process analysis and control of drilling operations. After receiving and starting drilling tasks, the method uses information board detection and PaddleOCR recognition services, and automatically saves construction information based on the identified drilling, closing, and sealing processes and construction parameters. When identifying the start of hole closing, the method enables the drill pipe withdrawal analysis service. When identifying the end of hole closing, the method stops the pipe withdrawal analysis service. The experimental results show that the recognition accuracy of the information board detection algorithm is 96%. The average time of PaddleOCR recognition algorithm is 17.51 ms, which is 25.25 ms lower than EasyOCR and 4.34 ms lower than Chinese OCR recognition algorithms, respectively; The accuracy of the PaddleOCR recognition algorithm has been improved by 5.75% and 2.29% compared to the other two recognition algorithms, respectively. The recall rate of the PaddleOCR recognition algorithm has been improved by 9.77% and 2.36% compared to the other two recognition algorithms, respectively. The pipe withdrawal analysis algorithm can effectively identify the number of pipes withdrawn on site, with an accuracy rate of approximately 95%.
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表 1 OCR识别效果对比
Table 1. Comparison of OCR identification effects
算法 准确率/% 召回率/% 时间/ms EasyOCR 88.15 86.26 42.76 ChineseOCR 91.61 93.67 21.85 PaddleOCR 93.90 96.03 17.51 
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[1] 姜德义,魏立科,王翀,等. 智慧矿山边缘云协同计算技术架构与基础保障关键技术探讨[J]. 煤炭学报,2020,45(1):484-492.JIANG Deyi,WEI Like,WANG Chong,et al. Discussion on the technology architecture and key basic support technology for intelligent mine edge-cloud collaborative computing[J]. Journal of China Coal Society,2020,45(1):484-492. [2] 谭章禄,吴琦,肖懿轩,等. 智慧矿山信息可视化研究[J]. 工矿自动化,2020,46(1):26-31.TAN Zhanglu,WU Qi,XIAO Yixuan,et al. Research on information visualization of smart mine[J]. Industry and Mine Automation,2020,46(1):26-31. [3] 王清峰,陈航. 瓦斯抽采智能化钻探技术及装备的发展与展望[J]. 工矿自动化,2018,44(11):18-24.WANG Qingfeng,CHEN Hang. Development and prospect on intelligent drilling technology and equipment for gas drainage[J]. Industry and Mine Automation,2018,44(11):18-24. [4] 吴克介,黄强,许金,等. 基于跨平台架构的全矿井瓦斯抽采智能管控软件设计[J]. 工矿自动化,2022,48(11):125-132.WU Kejie,HUANG Qiang,XU Jin,et al. Design of intelligent control software for whole mine gas extraction based on cross-platform architecture[J]. Journal of Mine Automation,2022,48(11):125-132. [5] 盛文燕,李勇,郝允领,等. 一种用于煤矿钻场的钻杆自动计数智能管理系统:CN202110513582.3[P]. 2021-08-06.SHENG Wenyan,LI Yong,HAO Yunling,et al. An intelligent management system for automatic counting of drill pipe in coal mine drilling field:CN202110513582.3[P]. 2021-08-06. [6] 张栋,姜媛媛. 基于改进MobileNetV2的钻杆计数方法[J]. 工矿自动化,2022,48(10):69-75.ZHANG Dong,JIANG Yuanyuan. Drill pipe counting method based on improved MobileNetV2[J]. Journal of Mine Automation,2022,48(10):69-75. [7] 潘涛. 煤矿生产系统集成的层次结构及其标准化问题研究[J]. 工矿自动化,2014,40(9):19-23.PAN Tao. Research of integrated architecture of coal mine production system and its standardization problems[J]. Industry and Mine Automation,2014,40(9):19-23. [8] 孙继平. 煤矿监控新技术与新装备[J]. 工矿自动化,2015,41(1):1-5.SUN Jiping. New technologies and new equipment of coal mine monitoring[J]. Industry and Mine Automation,2015,41(1):1-5. [9] 徐辉,贺耀宜. 一种煤矿井下监控视频图像预处理方法[J]. 工矿自动化,2016,42(1):32-34.XU Hui,HE Yaoyi. An image preprocessing method for underground monitoring video[J]. Industry and Mine Automation,2016,42(1):32-34. [10] 张立亚. 矿山智能视频分析与预警系统研究[J]. 工矿自动化,2017,43(11):16-20.ZHANG Liya. Research on intelligent video analysis and early warning system for mine[J]. Industry and Mine Automation,2017,43(11):16-20. [11] 吴文臻. 智能视频监控系统在煤矿井下的应用研究[J]. 煤炭技术,2016,35(4):271-273.WU Wenzhen. Application research of intelligent video surveillance system in coal mine[J]. Coal Technology,2016,35(4):271-273. [12] 程德强,钱建生,郭星歌,等. 煤矿安全生产视频AI识别关键技术研究综述[J]. 煤炭科学技术,2023,51(2):349-365.CHENG Deqiang,QIAN Jiansheng,GUO Xingge,et al. Review on key technologies of AI recognition for videos in coal mine[J]. Coal Science and Technology,2023,51(2):349-365. [13] 王国法,王虹,任怀伟,等. 智慧煤矿2025情景目标和发展路径[J]. 煤炭学报,2018,43(2):295-305.WANG Guofa,WANG Hong,REN Huaiwei,et al. 2025 scenarios and development path of intelligent coal mine[J]. Journal of China Coal Society,2018,43(2):295-305. [14] 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.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. [15] 苗续芝,陈伟,毕方明,等. 基于改进FOA−SVM的矿井火灾图像识别[J]. 计算机工程,2019,45(4):267-274.MIAO Xuzhi,CHEN Wei,BI Fangming,et al. Mine fire image recognition based on improved FOA-SVM[J]. Computer Engineering,2019,45(4):267-274. [16] 付文俊,杨富强,彭伟,等. 红庆梁煤矿安全智能视频系统[J]. 煤矿安全,2018,49(12):96-98.FU Wenjun,YANG Fuqiang,PENG Wei,et al. Application of safety intelligent video system in Hongqingliang Coal Mine[J]. Safety in Coal Mines,2018,49(12):96-98. [17] 牛云鹏,张立亚,贺云龙. 智能大采高综采工作面视频分析系统的研究与应用[J]. 中国煤炭,2020,46(6):40-44.NIU Yunpeng,ZHANG Liya,HE Yunlong. Research and application of video analysis system of intelligent fully mechanized working face with large mining height[J]. China Coal,2020,46(6):40-44. [18] 金利国,赵存会. 煤矿探水视频管理系统[J]. 工矿自动化,2018,44(9):102-104.JIN Liguo,ZHAO Cunhui. Water detection video management system of coal mine[J]. Industry and Mine Automation,2018,44(9):102-104. [19] ZHOU Junchi,JIANG Ping,ZOU Airu,et al. Ship target detection algorithm based on improved YOLOv5[J]. Journal of Marine Science and Engineering,2021,9(8):908-922. doi: 10.3390/jmse9080908 [20] KASPEREULAERS M,HAHN N,BERGER S,et al. Short communication:detecting heavy goods vehicles in rest areas in winter conditions using YOLOv5[J]. Algorithms,2021,14(4):114-125. doi: 10.3390/a14040114 [21] 邢宇驰,李大军,叶发茂. 基于YOLOv5的遥感图像目标检测[J]. 江西科学,2021,39(4):725-732.XING Yuchi,LI Dajun,YE Famao. Remote sensing image target detection based on YOLOv5[J]. Jiangxi Science,2021,39(4):725-732. -
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