Volume 49 Issue 5
May  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2023  >  49(5): 7-21
GONG Shixin, ZHAO Guorui, WANG Fei. Review on the application of machine vision perception theory and technology in coal industry[J]. Journal of Mine Automation，2023，49(5)：7-21.  doi: 10.13272/j.issn.1671-251x.2022100087
Citation: GONG Shixin, ZHAO Guorui, WANG Fei. Review on the application of machine vision perception theory and technology in coal industry[J]. Journal of Mine Automation，2023，49(5)：7-21.  doi: 10.13272/j.issn.1671-251x.2022100087
shu

Review on the application of machine vision perception theory and technology in coal industry

doi: 10.13272/j.issn.1671-251x.2022100087
  • 1.

    Intelligent Mining Branch, CCTEG Coal Mining Research Institute, Beijing 100013, China

  • 2.

    Mining Design Division, CCTEG Tiandi Science & Technology Co., Ltd., Beijing 100013, China

  • 3.

    Mining Research Branch, CCTEG China Coal Research Institute, Beijing 100013, China

  • 4.

    No.1 Coal Mine, Shaanxi Shanmei Huangling Mining Co., Ltd., Yan'an 727307, China

  • Received Date: 2022-10-29
  • Rev Recd Date: 2023-05-15
    • Available Online: 2023-05-30
    Abstract
  • Machine vision technology has positively improved coal mine safety monitoring methods and enhanced equipment automation levels. This article elaborates in detail on the principles of equipment information state perception based on machine vision in different scenarios and systems during the current intelligent construction process of coal mines. It summarizes the practical applications of machine vision perception technology in coal mine safety monitoring, picking recognition, coal rock recognition, positioning navigation, transportation detection, pose detection, and information measurement. The analysis points out that in the future, coal mine machine vision perception technology should deeply explore the understanding needs of mining face machine vision scenes. It is suggested to build a production full field of view monitoring and detection system, and improve the integrated monitoring effect of multiple spatiotemporal, multi-dimensional, and multivariate. It is suggested to improve the video autonomous monitoring and alarm capability, enhance visual guidance capability, and form a unified visual data management method for ground production management and operation systems. The key research should focus on technologies such as simultaneous spatiotemporal measurement of the pose of fully mechanized mining equipment (groups), perception of dynamic changes in the mining environment, full field of view monitoring and autonomous warning for production, and visual guidance and control of coal mining robots. It is pointed out that the coal mine machine vision perception technology still has challenges in explosion-proof or intrinsically safe intelligent vision sensors, efficient methods of visual measurement and analysis, the measurement precision of detection and recognition, and high-quality image annotation. Through the development of visual sensors with edge computing capabilities, a distributed vision measurement scheme is constructed to achieve accurate recognition and measurement of mining information in various complex environments. It can effectively improve the deeper integration and application of machine vision perception technology in the coal industry.

     

    • FullText(HTML)
  • loading
    • References(94)
  • [1]
    王国法,刘峰,孟祥军,等. 煤矿智能化(初级阶段)研究与实践[J]. 煤炭科学技术,2019,47(8):1-36.

    WANG Guofa,LIU Feng,MENG Xiangjun,et al. Research and practice on intelligent coal mine construction(primary stage)[J]. Coal Science and Technology,2019,47(8):1-36.
    [2]
    王国法,任怀伟,庞义辉,等. 煤矿智能化(初级阶段)技术体系研究与工程进展[J]. 煤炭科学技术,2020,48(7):1-27.

    WANG Guofa,REN Huaiwei,PANG Yihui,et al. Research and engineering progress of intelligent coal mine technical system in early stages[J]. Coal Science and Technology,2020,48(7):1-27.
    [3]
    王国法,任怀伟,赵国瑞,等. 智能化煤矿数据模型及复杂巨系统耦合技术体系[J]. 煤炭学报,2022,47(1):61-74.

    WANG Guofa,REN Huaiwei,ZHAO Guorui,et al. Digital model and giant system coupling technology system of smart coal mine[J]. Journal of China Coal Society,2022,47(1):61-74.
    [4]
    HASHMI A W,MALI H S,MEENA A,et al. Machine vision for the measurement of machining parameters:a review[J]. Materials Proceedings,2022,56(4):1939-1946.
    [5]
    WANG Tianhai,CHEN Bin,ZHANG Zhenqian,et al. Applications of machine vision in agricultural robot navigation:a review[J]. Computers and Electronics in Agriculture,2022,198:107085. doi: 10.1016/j.compag.2022.107085
    [6]
    BAROUD S,CHOKRI S,BELHAOUS S,et al. A brief review of graph convolutional neural network based learning for classifying remote sensing images[J]. Procedia Computer Science,2021,191(1):349-354.
    [7]
    TULBURE A-A,TULBURE A-A,DULF E-H. A review on modern defect detection models using DCNNs-Deep convolutional neural networks[J]. Journal of Advanced Research,2022,35:33-48. doi: 10.1016/j.jare.2021.03.015
    [8]
    SABZI S,ABBASPOUR-GILANDEH Y,JAVADIKIA H. Machine vision system for the automatic segmentation of plants under different lighting conditions[J]. Biosystems Engineering,2017,161:157-173. doi: 10.1016/j.biosystemseng.2017.06.021
    [9]
    ELHASSAN M,KONSTANTINOS S,GARETH H. A review of visualisation-as-explanation techniques for convolutional neural networks and their evaluation[J]. Displays,2022,73:102239. doi: 10.1016/j.displa.2022.102239
    [10]
    VALIZADEH M,WOLFF S J. Convolutional neural network applications in additive manufacturing:a review[J]. Advances in Industrial and Manufacturing Engineering,2022,4:100072. doi: 10.1016/j.aime.2022.100072
    [11]
    JIAO Jinyang,ZHAO Ming,LIN Jing,et al. A comprehensive review on convolutional neural network in machine fault diagnosis[J]. Neurocomputing,2020,417:36-63. doi: 10.1016/j.neucom.2020.07.088
    [12]
    王伯君. 袁大滩煤矿视频监控的组成与功能实现[J]. 信息通信,2020(8):115-117.

    WANG Bojun. Composition and function realization of video surveillance in Yuandatan Coal Mine[J]. Information & Communications,2020(8):115-117.
    [13]
    王宇,吴智恒,刘泓滨,等. 机器视觉的煤自燃智能预警系统设计[J]. 煤矿机械,2016,37(8):16-17.

    WANG Yu,WU Zhiheng,LIU Hongbin,et al. Design of coal spontaneous combustion intelligent warning and monitoring system based on machine vision[J]. Coal Mine Machinery,2016,37(8):16-17.
    [14]
    曹玉超,范伟强. 基于不同深度识别算法的矿井水位标尺刻度识别性能分析与研究[J]. 煤炭学报,2019,44(11):3529-3538.

    CAO Yuchao,FAN Weiqiang. Performance analysis and research of mine water level scale recognition based on different depth recognition algorithms[J]. Journal of China Coal Society,2019,44(11):3529-3538.
    [15]
    CHEN Wei,SUN Tongfeng,LI Ming,et al. A new image co-segmentation method using saliency detection for surveillance image of coal miners[J]. Computers & Electrical Engineering,2014,40(8):227-235.
    [16]
    刘春梅,李辉. 煤矿开采用掘进机人员识别系统设计与研究[J]. 内蒙古农业大学学报(自然科学版),2020,41(4):76-79.

    LIU Chunmei,LI Hui. Design of the roadheaders used in the personnel identification system[J]. Journal of Inner Mongolia Agricultural University (Natural Science Edition),2020,41(4):76-79.
    [17]
    王成军,严晨. 机器视觉技术在分拣系统中的应用研究综述[J]. 制造技术与机床,2020(5):32-37.

    WANG Chengjun,YAN Chen. Summary of application research of machine vision technology in sorting system[J]. Manufacturing Technology & Machine Tool,2020(5):32-37.
    [18]
    IGATHINATHANE C,ULUSOY U. Machine vision methods based particle size distribution of ball- and gyro-milled lignite and hard coal[J]. Powder Technology,2016,297:71-80. doi: 10.1016/j.powtec.2016.03.032
    [19]
    DOU Dongyang,WU Wenze,YANG Jianguo,et al. Classification of coal and gangue under multiple surface conditions via machine vision and relief-SVM[J]. Powder Technology,2019,356:1024-1028. doi: 10.1016/j.powtec.2019.09.007
    [20]
    饶中钰,吴景涛,李明. 煤矸石图像分类方法[J]. 工矿自动化,2020,46(3):69-73.

    RAO Zhongyu,WU Jingtao,LI Ming. Coal-gangue image classification method[J]. Industry and Mine Automation,2020,46(3):69-73.
    [21]
    SUN Jiping,CHEN Wei,MA Fengying,et al. Classification of infrared monitor images of coal using a feature texture statistics and improved BP network[J]. Journal of China University of Mining & Technology,2007,17(4):489-493.
    [22]
    LYU Ziqi,WANG Weidong,XU Zhiqiang,et al. Cascade network for detection of coal and gangue in the production context[J]. Powder Technology,2021,377:361-371. doi: 10.1016/j.powtec.2020.08.088
    [23]
    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,2021,102:104242. doi: 10.1016/j.engappai.2021.104242
    [24]
    WANG Xinquan,WANG Shuang,GUO Yongcun,et al. Dielectric and geometric feature extraction and recognition method of coal and gangue based on VMD-SVM[J]. Powder Technology,2021,392:241-250. doi: 10.1016/j.powtec.2021.06.057
    [25]
    郭永存,王希,何磊,等. 基于TW−RN优化CNN的煤矸识别方法研究[J]. 煤炭科学技术,2022,50(1):228-236. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201023

    GUO Yongcun,WANG Xi,HE Lei,et al. Research on coal and gangue recognition method based on TW-RN optimized CNN[J]. Coal Science and Technology,2022,50(1):228-236. doi: 10.3969/j.issn.0253-2336.2022.1.mtkxjs202201023
    [26]
    JEMWA G T,ALDRICH C. Estimating size fraction categories of coal particles on conveyor belts using image texture modeling methods[J]. Expert Systems with Applications,2012,39:7947-7960. doi: 10.1016/j.eswa.2012.01.104
    [27]
    王鹏,曹现刚,夏晶,等. 基于机器视觉的多机械臂煤矸石分拣机器人系统研究[J]. 工矿自动化,2019,45(9):47-53.

    WANG Peng,CAO Xiangang,XIA Jing,et al. Research on multi-manipulator coal and gangue sorting robot system based on machine vision[J]. Industry and Mine Automation,2019,45(9):47-53.
    [28]
    孙磊. 煤矿自动选矸机器人系统设计与应用[J]. 自动化应用,2021(5):39-43.

    SUN Lei. Design and application of coal mine automatic waste selection robot system[J]. Automation Application,2021(5):39-43.
    [29]
    马宏伟,孙那新,张烨,等. 煤矸石分拣机器人动态目标稳定抓取轨迹规划[J]. 工矿自动化,2022,48(4):20-30.

    MA Hongwei,SUN Naxin,ZHANG Ye,et al. Track planning of coal gangue sorting robot for dynamic target stable grasping[J]. Journal of Mine Automation,2022,48(4):20-30.
    [30]
    司垒,王忠宾,熊祥祥,等. 基于改进U−net网络模型的综采工作面煤岩识别方法[J]. 煤炭学报,2021,46(增刊1):578-589.

    SI Lei,WANG Zhongbin,XIONG Xiangxiang,et al. Coal-rock recognition method of fully-mechanized coal mining face based on improved U-net network model[J]. Journal of China Coal Society,2021,46(S1):578-589.
    [31]
    刘俊利,赵豪杰,李长有. 基于采煤机滚筒截割振动特性的煤岩识别方法[J]. 煤炭科学技术,2013,41(10):93-95,116.

    LIU Junli,ZHAO Haojie,LI Changyou. Coal-rock recognition method based on cutting vibration features of coal shearer drums[J]. Coal Science and Technology,2013,41(10):93-95,116.
    [32]
    王元军,王明松,田山军,等. 基于卡尔曼滤波与随机森林的煤岩识别研究[J]. 煤炭技术,2021,40(12):208-211.

    WANG Yuanjun,WANG Mingsong,TIAN Shanjun,et al. Study on recognition of coal and rock based on Kalman filter and random forest[J]. Coal Technology,2021,40(12):208-211.
    [33]
    张强,张润鑫,刘峻铭,等. 煤矿智能化开采煤岩识别技术综述[J]. 煤炭科学技术,2022,50(2):1-26.

    ZHANG Qiang,ZHANG Runxin,LIU Junming,et al. Review on coal and rock identification technology for intelligent mining in coal mines[J]. Coal Science and Technology,2022,50(2):1-26.
    [34]
    孙继平,佘杰. 基于小波的煤岩图像特征抽取与识别[J]. 煤炭学报,2013,38(10):1900-1904.

    SUN Jiping,SHE Jie. Wavelet-based coal-rock image feature extraction and recognition[J]. Journal of China Coal Society,2013,38(10):1900-1904.
    [35]
    孙继平,陈浜. 基于双树复小波域统计建模的煤岩识别方法[J]. 煤炭学报,2016,41(7):1847-1858.

    SUN Jiping,CHEN Bang. An approach to coal-rock recognition via statistical modeling in dual-tree complex wavelet domain[J]. Journal of China Coal Society,2016,41(7):1847-1858.
    [36]
    孙继平,陈浜. 基于小波域非对称广义高斯模型的煤岩识别算法[J]. 煤炭学报,2015,40(增刊2):568-575.

    SUN Jiping,CHEN Bang. A coal-rock recognition algorithm using wavelet-domain asymmetric generalized Gaussian models[J]. Journal of China Coal Society,2015,40(S2):568-575.
    [37]
    孙继平,佘杰. 基于支持向量机的煤岩图像特征抽取与分类识别[J]. 煤炭学报,2013,38(增刊2):508-512.

    SUN Jiping,SHE Jie. Coal-rock imagefeature extraction and recognition based on support vector machine[J]. Journal of China Coal Society,2013,38(S2):508-512.
    [38]
    吕红杰. 卷积神经网络在煤岩图像识别中的应用研究[J]. 科技创新导报,2019,16(9):137-139.

    LYU Hongjie. Application of convolutional neural networks in coal and rock image recognition[J]. Science and Technology Innovation Herald,2019,16(9):137-139.
    [39]
    伍云霞,田一民. 基于字典学习的煤岩图像特征提取与识别方法[J]. 煤炭学报,2016,41(12):3190-3196.

    WU Yunxia,TIAN Yimin. Method of coal-rock image feature extraction and recognition based on dictionary learning[J]. Journal of China Coal Society,2016,41(12):3190-3196.
    [40]
    黄韶杰,刘建功. 基于高斯混合聚类的煤岩识别技术研究[J]. 煤炭学报,2015,40(增刊2):576-582.

    HUANG Shaojie,LIU Jiangong. Research of coal-rock recognition technology based on GMM clustering analysis[J]. Journal of China Coal Society,2015,40(S2):576-582.
    [41]
    王超,张强. 基于LBP和GLCM的煤岩图像特征提取与识别方法[J]. 煤矿安全,2020,51(4):129-132.

    WANG Chao,ZHANG Qiang. Coal rock image feature extraction and recognition method based on LBP and GLCM[J]. Safety in Coal Mines,2020,51(4):129-132.
    [42]
    王燕平. 基于改进YOLOv3的煤岩界线识别及采高仿人智能控制研究[D]. 太原: 中北大学, 2021.

    WANG Yanping. Coal-rock boundary identification based on improved YOLOv3 and research on human-simulating intelligent control of mining height[D]. Taiyuan: Central North University, 2021.
    [43]
    黄韶杰. 基于聚类的煤岩分界图像识别技术研究[D]. 北京: 中国矿业大学（北京）, 2016.

    HUANG Shaojie. Research on coal rock boundary image recognition technology based on clustering[D]. Beijing: China University of Mining and Technology-Beijing, 2016.
    [44]
    张羽飞,马宏伟,毛清华,等. 视觉与惯导融合的煤矿移动机器人定位方法[J]. 工矿自动化,2021,47(3):46-52.

    ZHANG Yufei,MA Hongwei,MAO Qinghua,et al. Coal mine mobile robot positioning method based on fusion of vision and inertial navigation[J]. Industry and Mine Automation,2021,47(3):46-52.
    [45]
    薛旭升,张旭辉,毛清华,等. 基于双目视觉的掘进机器人定位定向方法研究[J]. 西安科技大学学报,2020,40(5):781-789.

    XUE Xusheng,ZHANG Xuhui,MAO Qinghua,et al. Localization and orientation method of roadheader robot based on binocular vision[J]. Journal of Xi'an University of Science and Technology,2020,40(5):781-789.
    [46]
    马宏伟,王岩,杨林. 煤矿井下移动机器人深度视觉自主导航研究[J]. 煤炭学报,2020,45(6):2193-2206.

    MA Hongwei,WANG Yan,YANG Lin. Research on depth vision based mobile robot autonomous navigation in underground coal mine[J]. Journal of China Coal Society,2020,45(6):2193-2206.
    [47]
    程健. 煤矿巷道机器人管线视觉辅助定位与导航方法研究[J]. 煤炭科学技术,2020,48(7):226-232.

    CHENG Jian. Study on pipeline vision-aided positioning and navigation method for coal mine tunnel robot[J]. Coal Science and Technology,2020,48(7):226-232.
    [48]
    张旭辉, 沈奇峰, 杨文娟, 等. 基于三激光点标靶的掘进机机身视觉定位技术研究[J/OL]. 电子测量与仪器学报: 1-9. [2022-08-27]. https://kns.cnki.net/kcms/detail/11. 2488.TN.20220612.1403.024.html.

    ZHANG Xuhui, SHEN Qifeng, YANG Wenjuan, et al. Research on visual positioning technology of roadheader body based on three laser point target[J/OL]. Journal of Electronic Measurement and Instrumentation: 1-9. [2022-08-27]. https://kns.cnki.net/kcms/detail/ 11.2488.TN. 20220612.1403.024.html.
    [49]
    张旭辉,赵建勋,杨文娟,等. 悬臂式掘进机视觉导航与定向掘进控制技术[J]. 煤炭学报,2021,46(7):2186-2196.

    ZHANG Xuhui,ZHAO Jianxun,YANG Wenjuan,et al. Vision-based navigation and directional heading control technologies of boom-type roadheader[J]. Journal of China Coal Society,2021,46(7):2186-2196.
    [50]
    韩江洪,卫星,陆阳,等. 煤矿井下机车无人驾驶系统关键技术[J]. 煤炭学报,2020,45(6):2104-2115.

    HAN Jianghong,WEI Xing,LU Yang,et al. Driverless technology of underground locomotive in coal mine[J]. Journal of China Coal Society,2020,45(6):2104-2115.
    [51]
    王星. 基于视觉的煤矿井下带式输送机异常状态监测方法研究[D]. 太原: 太原科技大学, 2017.

    WANG Xing. Research on abnormal state monitoring method of coal mine underground belt conveyor based on vision[D]. Taiyuan: Taiyuan University of Science and Technology, 2017.
    [52]
    王涛. 机器视觉技术在煤矿胶带运输系统中的应用[J]. 能源科技,2021,19(2):34-40.

    WANG Tao. Application of the machine vision technology in the belt conveyor system for coal mines[J]. Energy Science and Technology,2021,19(2):34-40.
    [53]
    顼熙亮. 基于机器视觉的矿用皮带运输机故障智能检测系统[J]. 煤矿现代化,2020(5):212-215.

    XU Xiliang. Intelligent fault detection system of mine belt conveyor based on machine vision[J]. Coal Mine Modernization,2020(5):212-215.
    [54]
    董征,张旭辉,王泰华,等. 基于机器视觉的矿用带式输送机跑偏故障检测系统[J]. 智能矿山,2022,3(2):60-65.

    DONG Zheng,ZHANG Xuhui,WANG Taihua,et al. Mine belt conveyor deviation detection system based on machine visio[J]. Journal of Intelligent Mine,2022,3(2):60-65.
    [55]
    ZHANG Mengchao,JIANG Kai,CAO Yueshuai,et al. A deep learning-based method for deviation status detection in intelligent conveyor belt system[J]. Journal of Cleaner Production,2022,363:132575. doi: 10.1016/j.jclepro.2022.132575
    [56]
    刘尹霞,李斯,王东,等. 煤炭传输带边缘磨损机器视觉无损在线检测[J]. 露天采矿技术,2015(5):39-41.

    LIU Yinxia,LI Si,WANG Dong,et al. Coal conveying belt edge wear machine vision non-destructive on-line detection[J]. Opencast Mining Technology,2015(5):39-41.
    [57]
    胡明明,乔铁柱,郑补祥. 基于NI机器视觉的胶带纵向撕裂检测系统[J]. 仪表技术与传感器,2013(11):41-43.

    HU Mingming,QIAO Tiezhu,ZHENG Buxiang. Inspection system of belt longitudinal tear based on NI machine vision[J]. Instrument Technique and Sensor,2013(11):41-43.
    [58]
    郝志伟. 基于机器视觉识别技术的煤矿带式输送自适应控制系统设计[J]. 煤炭工程,2019,51(9):37-41.

    HAO Zhiwei. Coal mine belt conveyor adaptive control system based on machine vision recognition technology[J]. Coal Engineering,2019,51(9):37-41.
    [59]
    QIAO Tiezhu,CHEN Lulu,PANG Yusong,et al. Integrative binocular vision detection method based on infrared and visible light fusion for conveyor belts longitudinal tear[J]. Measurement,2017,110:192-201. doi: 10.1016/j.measurement.2017.06.032
    [60]
    YANG Yanli,MIAO Changyun,LI Xianguo,et al. On-line conveyor belts inspection based on machine vision[J]. Optik,2014,125(19):5803-5807. doi: 10.1016/j.ijleo.2014.07.070
    [61]
    LI Xianguo,SHEN Lifang,MING Zixu,et al. Laser-based on-line machine vision detection for longitudinal rip of conveyor belt[J]. Optik,2018,168:360-369. doi: 10.1016/j.ijleo.2018.04.053
    [62]
    CHE Jian,QIAO Tiezhu,YANG Yi,et al. Longitudinal tear detection method of conveyor belt based on audio-visual fusion[J]. Measurement,2021,176:109152. doi: 10.1016/j.measurement.2021.109152
    [63]
    张和平. 基于机器视觉的矿用带式输送机堆煤故障监控系统研究[J]. 煤炭科技,2020,41(1):89-92. doi: 10.3969/j.issn.1008-3731.2020.01.029

    ZHANG Heping. Research on fault monitoring system of mine belt conveyor based on machine vision[J]. Coal Science & Technology Magazine,2020,41(1):89-92. doi: 10.3969/j.issn.1008-3731.2020.01.029
    [64]
    BAO Nengsheng,KUANG Huiling,SIMEONE A,et al. A machine vision-based automatic inspection system for power station coal bunkers maintenance[J]. Procedia CIRP,2021,103:250-255. doi: 10.1016/j.procir.2021.10.040
    [65]
    毛清华,毛金根,马宏伟,等. 矿用带式输送机智能监测系统研究[J]. 工矿自动化,2020,46(6):48-52,58.

    MAO Qinghua,MAO Jingen,MA Hongwei,et al. Research on intelligent monitoring system of mine-used belt conveyor[J]. Industry and Mine Automation,2020,46(6):48-52,58.
    [66]
    马宏伟,杨文娟,张旭辉. 带式输送机托辊红外图像分割与定位算法[J]. 西安科技大学学报,2017,37(6):892-898.

    MA Hongwei,YANG Wenjuan,ZHANG Xuhui. Segmentation and location algorithm for infrared image of roller on conveyor belt[J]. Journal of Xi'an University of Science and Technology,2017,37(6):892-898.
    [67]
    李帅帅,任怀伟. 综采工作面“三机”设备位姿测量技术研究现状与展望[J]. 煤炭科学技术,2020,48(9):218-226.

    LI Shuaishuai,REN Huaiwei. Research status and development trend of position and posture measurement technology on hydraulic support,scraper conveyor,shearer in fully-mechanized mining face[J]. Coal Science and Technology,2020,48(9):218-226.
    [68]
    许金星. 机器视觉的液压支架姿态角度测量系统设计[J]. 煤矿机械,2019,40(9):11-13.

    XU Jinxing. Design of attitude angle measurement system for hydraulic support based on machine vision[J]. Coal Mine Machinery,2019,40(9):11-13.
    [69]
    张旭辉,王冬曼,杨文娟. 基于视觉测量的液压支架位姿检测方法[J]. 工矿自动化,2019,45(3):56-60.

    ZHANG Xuhui,WANG Dongman,YANG Wenjuan. Position detection method of hydraulic support based on vision measurement[J]. Industry and Mine Automation,2019,45(3):56-60.
    [70]
    任怀伟,李帅帅,赵国瑞,等. 基于深度视觉原理的工作面液压支架支撑高度与顶梁姿态角测量方法研究[J]. 采矿与安全工程学报,2022,39(1):72-81,93.

    REN Huaiwei,LI Shuaishuai,ZHAO Guorui,et al. Measurement method of support height and roof beam posture angles for working face hydraulic support based on depth vision[J]. Journal of Mining & Safety Engineering,2022,39(1):72-81,93.
    [71]
    王渊,李红卫,郭卫,等. 基于图像识别的液压支架护帮板收回状态监测方法[J]. 工矿自动化,2019,45(2):47-53.

    WANG Yuan,LI Hongwei,GUO Wei,et al. Monitoring method of recovery state of hydraulic support guard plate based on image recognition[J]. Industry and Mine Automation,2019,45(2):47-53.
    [72]
    刘鹏坤,王聪. 基于机器视觉的长壁工作面直线度测量算法研究[J]. 矿业科学学报,2017,2(3):267-273.

    LIU Pengkun,WANG Cong. Straightness measurement algorithm based on machine vision for coal longwall face[J]. Journal of Mining Science and Technology,2017,2(3):267-273.
    [73]
    康红普,姜鹏飞,高富强,等. 掘进工作面围岩稳定性分析及快速成巷技术途径[J]. 煤炭学报,2021,46(7):2023-2045.

    KANG Hongpu,JIANG Pengfei,GAO Fuqiang,et al. Analysis on stability of rock surrounding heading faces and technical approaches for rapid heading[J]. Journal of China Coal Society,2021,46(7):2023-2045.
    [74]
    雷孟宇,张旭辉,杨文娟,等. 煤矿掘进装备视觉位姿检测与控制研究现状与趋势[J]. 煤炭学报,2021,46(增刊2):1135-1148.

    LEI Mengyu,ZHANG Xuhui,YANG Wenjuan,et al. Current status and trend of research on visual pose detection and control of heading equipment in coal mines[J]. Journal of China Coal Society,2021,46(S2):1135-1148.
    [75]
    杨文娟,马宏伟,张旭辉. 悬臂式掘进机截割头姿态视觉检测系统[J]. 煤炭学报,2018,43(增刊2):581-590.

    YANG Wenjuan,MA Hongwei,ZHANG Xuhui. Attitude measurement system of cutting head for boom-type roadheader based on vision measurement[J]. Journal of China Coal Society,2018,43(S2):581-590.
    [76]
    张旭辉,刘永伟,杨文娟,等. 矿用悬臂式掘进机截割头姿态视觉测量系统[J]. 工矿自动化,2018,44(8):63-67.

    ZHANG Xuhui,LIU Yongwei,YANG Wenjuan,et al. Vision measurement system for cutting head attitude of mine-used boom-type roadheader[J]. Industry and Mine Automation,2018,44(8):63-67.
    [77]
    张旭辉,张超,杨文娟,等. 悬臂式掘进机可视化辅助截割系统研制[J]. 煤炭科学技术,2018,46(12):21-26.

    ZHANG Xuhui,ZHANG Chao,YANG Wenjuan,et al. Research and development of visual auxiliary cutting system for cantilever roadheader[J]. Coal Science and Technology,2018,46(12):21-26.
    [78]
    张旭辉,谢楠,张超,等. 悬臂式掘进机截割头位姿视觉测量系统改进[J]. 工矿自动化,2021,47(7):1-7.

    ZHANG Xuhui,XIE Nan,ZHANG Chao,et al. Improvement of vision measurement system for cutting head position of boom-type roadheader[J]. Industry and Mine Automation,2021,47(7):1-7.
    [79]
    张旭辉,张楷鑫,张超,等. 悬臂式掘进机视觉位姿检测系统外参标定方法[J]. 机械科学与技术,2022,41(12):1928-1935.

    ZHANG Xuhui,ZHANG Kaixin,ZHANG Chao,et al. Calibrating external parameters of visual position detection system of cantilever roadheader[J]. Mechanical Science and Technology for Aerospace Engineering,2022,41(12):1928-1935.
    [80]
    赵建勋,杨文娟,张旭辉,等. 悬臂式掘进机井下精确定位方法及其视觉测量技术[J]. 煤炭科学技术,2021,49(12):192-201.

    ZHAO Jianxun,YANG Wenjuan,ZHANG Xuhui,et al. Study on accurate positioning method and its visual measurement technology of cantilever roadheader underground[J]. Coal Science and Technology,2021,49(12):192-201.
    [81]
    张超,张旭辉,杜昱阳,等. 基于双目视觉的悬臂式掘进机位姿测量技术研究[J]. 煤炭科学技术,2021,49(11):225-235.

    ZHANG Chao,ZHANG Xuhui,DU Yuyang,et al. Measuring technique of cantilever roadheader position based on binocular stereo vision[J]. Coal Science and Technology,2021,49(11):225-235.
    [82]
    张旭辉,赵建勋,张超,等. 悬臂式掘进机视觉伺服截割控制系统研究[J]. 煤炭科学技术,2022,50(2):263-270.

    ZHANG Xuhui,ZHAO Jianxun,ZHANG Chao,et al. Study on visual servo control system for cutting of cantilever roadheader[J]. Coal Science and Technology,2022,50(2):263-270.
    [83]
    柳学猛,张凯, 马跃. 基于视觉测量的挖掘机工作装置姿态测量系统[J]. 中国矿业,2022,31(9):89-94.

    LIU Xuemeng,ZHANG Kai,MA Yue. Application status and trend of roadheader pose detection based on machine vision[J]. China Mining Magazine,2022,31(9):89-94.
    [84]
    张旭辉,周创,张超,等. 基于视觉测量的快速掘进机器人纠偏控制研究[J]. 工矿自动化,2020,46(9):21-26.

    ZHANG Xuhui,ZHOU Chuang,ZHANG Chao,et al. Research on deviation correction control of rapid tunneling robot based on vision measurement[J]. Industry and Mine Automation,2020,46(9):21-26.
    [85]
    王昱栋,代伟,马小平. 基于机器视觉的非接触式锚杆异常快速检测方法[J]. 工矿自动化,2021,47(4):13-18.

    WANG Yudong,DAI Wei,MA Xiaoping. Rapid detection method of bolt abnormality based on machine vision[J]. Industrial and Mining Automation,2021,47(4):13-18.
    [86]
    李萍,任安祥. 基于机器视觉的带送煤炭体积测量方法研究[J]. 工矿自动化,2018,44(4):24-29.

    LI Ping,REN Anxiang. Research on volume measurement method of coal on belt conveying based on machine vision[J]. Industry and Mine Automation,2018,44(4):24-29.
    [87]
    胡而已. 融合激光扫描与机器视觉的煤流量测量研究[J]. 煤炭工程,2021,53(11):146-151.

    HU Eryi. Coal flow measurement based on laser scanning and machine vision[J]. Coal Engineering,2021,53(11):146-151.
    [88]
    杨春雨,顾振,张鑫,等. 基于深度学习的带式输送机煤流量双目视觉测量[J]. 仪器仪表学报,2021,41(8):164-174.

    YANG Chunyu,GU Zhen,ZHANG Xin,et al. Binocular vision measurement of coal flow of belt conveyors based on deep learning[J]. Chinese Journal of Scientific Instrument,2021,41(8):164-174.
    [89]
    QIU Zhaoyu,DOU Dongyang,ZHOU Deyang,et al. On-line prediction of clean coal ash content based on image analysis[J]. Measurement,2021,173:108663. doi: 10.1016/j.measurement.2020.108663
    [90]
    ZHANG Zelin,YANG Jianguo,WANG Yuling,et al. Ash content prediction of coarse coal by image analysis and GA-SVM[J]. Powder Technology,2014,268:429-435. doi: 10.1016/j.powtec.2014.08.044
    [91]
    ZHANG Zelin,LIU Yang,HU Qi,et al. Multi-information online detection of coal quality based on machine vision[J]. Powder Technology,2020,374:250-262. doi: 10.1016/j.powtec.2020.07.040
    [92]
    吴智峰,柴鑫,王亚波,等. 基于机器视觉非接触测量外螺纹尺寸系统[J]. 煤矿机械,2018,39(8):171-172.

    WU Zhifeng,CHAI Xin,WANG Yabo,et al. Measurement of external thread size system based on machine vision non-contact[J]. Coal Mine Machinery,2018,39(8):171-172.
    [93]
    GAO Yuan,QIAO Tiezhu,ZHANG Haitao,et al. A contact-less measuring speed system of belt conveyor based on machine vision and machine learning[J]. Measurement,2019,139:127-133. doi: 10.1016/j.measurement.2019.03.030
    [94]
    田原. 浅谈机器视觉技术在煤矿中的应用前景[J]. 工矿自动化,2010,36(5):30-32.

    TIAN Yuan. Brief discussion of application prospect of technology of machine vision in coal mine[J]. Industry and Mine Automation,2010,36(5):30-32.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (266) PDF downloads(99) Cited by()
    Proportional views
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co. Ltd.  Visits：    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return