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%。
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information on the information board.The drill pipe withdrawal analysis is used to analyze the number of drill pipes withdrawn during the closing drilling phase,thereby achieving the full process analysis and control of drilling operations.After receiving and starting drilling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%.
作者
胡金成
张立斌
蒋泽
姚超修
蒋志龙
王正义
HU Jincheng;ZHANG Libin;JIANG Ze;YAO Chaoxiu;JIANG Zhilong;WANG Zhengyi(CCTEG Changzhou Research Institute,Changzhou 213015,China;Tiandi(Changzhou)Automation Co.,Ltd.,Changzhou 213015,China;School of Civil Engineering and Architecture,Changzhou Institute of Technology,Changzhou 213032,China)
出处
《工矿自动化》
CSCD
北大核心
2023年第11期167-172,共6页
Journal Of Mine Automation
基金
江苏省产学研合作项目(BY2022109)
天地科技股份有限公司科技创新创业资金专项项目(2020-TD-ZD010)
中煤科工集团常州研究院科研项目(2022TY2012)。
关键词
瓦斯抽采钻场
AI视频分析
打钻
退杆分析
信息牌检测
OCR识别
全过程分析
gas extraction drilling site
AI video analysis
drilling
pipe withdrawal analysis
information board detection
OCR recognition
whole process analysis
