img

Wechat

Adv. Search

Gold Science and Technology ›› 2022, Vol. 30 ›› Issue (1): 131-140.doi: 10.11872/j.issn.1005-2518.2022.01.135

• Mining Technology and Mine Management • Previous Articles     Next Articles

Mine-Loading Measurement System of Underground Locomotive Based on Image Recognition

Yupeng ZHANG(),Fuji WU,Yi GUO   

  1. Nonferrous Metal Mining and Metallurgy Equipment Work Design Center,Ganzhou Nonferrous Metallurgy Research Institute Co. ,Ltd. ,Ganzhou 341000,Jiangxi,China
  • Received:2021-09-26 Revised:2022-01-10 Online:2022-02-28 Published:2022-04-25

Abstract:

The ore yield is an important standard for mining enterprises to formulate production plans.At present,the vast majority of underground mines estimate ore yield by manually counting the number of ore carrying vehicles.The measurement error is large,which seriously affects the enterprises to formulate production plans.In order to solve the problem of large measurement error and improve the accuracy of ore yield estimation,a set of underground machine on-board ore yield measurement system based on image recognition was designed and developed in this paper.In this paper,the method of image recognition combined with density model modeling was used to form a volume model through three-dimensional reconstruction of ore pile image information,and an image feature density library was built to form a complete set of underground machine on-board ore quantity measurement system.The system collects the internal image of the locomotive ore bucket through the depth camera,then extracts the feature information of the image and compares it with the image feature density library to obtain the density of the ore in the current ore bucket, and then generates a volume model from the three-dimensional reconstruction of the image to calculate the volume of the ore pile,and calculates the product of the volume and density of the ore pile to obtain the weight of the ore pile.The field repeated tests show that the metering system operates stably and reliably,and the calculation error of locomotive ore load is less than 5%.It solves the problem of ore yield estimation in mining enterprises,improves the calculation accuracy of ore yield,and brings detailed and reliable data for enterprises to formulate production plans.

Key words: image recognition, 3D reconstruction, density model, measurement system, underground locomotive, ore yield

CLC Number: 

  • TP274+.5

Fig.1

Structural diagram of underground vehicle mine-loading measurement system"

Fig.2

Camera integration device"

Fig.3

Installation diagram of hopper and depth camera"

Fig.4

Depth map definition"

Fig.5

Effect diagram of mining vehicle image"

Fig.6

Interface of real time monitoring system of ore loading"

Table 1

Total weight,ore volume and average density of partial mining vehicle"

矿车总重量/kg矿石重量/kg矿石体积/m3

平均密度

/(×103 kg·m-3

1 7601 0320.8411.227
1 7951 0670.8711.224
1 7951 0670.8681.229
1 8661 1380.5312.142
1 8661 1380.8111.404
1 9731 2450.6202.009
1 9731 2450.6981.783
1 9731 2450.6561.898
1 9731 2450.7441.673
1 8901 1620.6401.814
1 8901 1620.6531.780
1 9701 2420.7381.683
1 9701 2420.7571.640
1 9891 2610.4752.653
2 1121 3840.8511.626
2 0421 3140.8161.611

Fig.7

Line graph of average density of ore heap in mining vehicle"

Table 2

Corresponding relationship between partial image file names and average density"

图像文件名平均密度/(×103 kg·m-3
2020-11-13-09-37-58-887_RGB.png1.227
2020-11-13-09-38-02-599_RGB.png1.224
2020-11-13-09-38-02-789_RGB.png1.229
2020-11-13-09-38-33-286_RGB.png2.142
2020-11-13-09-38-36-077_RGB.png1.404
2020-11-13-09-38-41-159_RGB.png2.009
2020-11-13-09-38-42-699_RGB.png1.784
2020-11-13-09-38-45-640_RGB.png1.898
2020-11-13-09-38-46-571_RGB.png1.674
2020-11-13-09-38-53-163_RGB.png1.815
2020-11-13-09-38-58-121_RGB.png1.781
2020-11-13-09-38-59-648_RGB.png1.684
2020-11-13-09-39-08-730_RGB.png1.640
2020-11-13-09-53-39-621_RGB.png2.653
2020-11-13-09-53-50-114_RGB.png1.626
2020-11-13-09-54-03-006_RGB.png1.611

Table 3

Comparison between measurement results of partial metering system and weighing results of track scale"

矿石体积

/m3

计量系统测量结果

/kg

轨道衡称重结果

/kg

相对误差

/%

0.7872011 2151 1446.21
0.7269661 1671 0986.28
0.4245921 0221 088-6.07
0.7690641 1011 0386.07
0.7493071 1601 0995.55
0.7421111 1611 0975.83
0.7872051 0941 0326.01
0.7927871 2301 1675.40
0.4753871 1591 229-5.70
0.8513791 5061 4215.98
0.8155181 2591 314-4.19
0.7977621 1141 182-5.75
0.8485251 4041 3305.56
0.8168211 3851 3314.06
0.8462121 4391 3566.12
0.8631801 4141 3484.90
Baklanova O E, Baklanov M A,2016.Methods and algorithms of image recognition for mineral rocks in the mining industry[J].Advances in Swarm Intelligence,9713:253-262.
Chen Shaojie, Li Guangli, Zhang Wei, al et,2011.Land use classification in coal mining area using remote sensing images based on multiple classifier combination[J].Journal of China University of Mining & Technology,40(2):273-278.
Dai Changlu,2020.Comprehensive application of XRT ray and image intelligent concentrator in wolframite mine[J].China Metal Bulletin,(11):187-188.
Di Wangping, Wu Zhihu,2021.Preconcentration and discarding technology of intelligent photoelectric dressing equipment[J].Nonferrous Metals:Mieral Processing Section,(1):117-121.
Du Peijun, Liu Sicong, Zheng Hui,2012.Land cover change detection over mining areas based on support vector machine[J].Journal of China University of Mining & Technology,41(2):262-267.
Guo Yi, Wu Fuji, Zhong Yi, al et,2021.Research on intelligent cleaning device for bottom of tipping-type car based on image recognition technology[J].China Tungsten Industry,36(2):76-80.
He Jie, Wang Guimei, Liu Jiehui, al et,2020.Volume measurement of coal volume on belt conveyor based on image processing[J].Acta Metrologica Sinica,41(12):1516-1520.
Li Guoqing, Li Bao, Hu Nailian, al et,2017.Optimization model of mining operation scheduling for underground metal mines[J].Chinese Journal of Engineering,39(3):342-348.
Li Hexian,2020.Microscopic measurement and refreshing device for CCD camera[J].Journal of Chongqing University of Science and Technology(Natural Sciences Edition),22(2):93-96.
Li Xingdong, Zhang Rui,2021.On differential element method and its principle[J].Studies in College Mathematics,24(1):80-83.
Liang Le,2019.Research on Measurement Method of Irregular Object Volume Based on Binocular Stereo Vision[D].Xi’an:Xi’an University of Technology.
Ling Xiaoming, Guo Ruixin, Liu Guangting, al et,2021.Pedestrian detection research based on multi-feature cascade of PCA dimension reduction[J].Manufacturing Automation,43(3):32-34,76.
Patel A K, Chatterjee S, Gorai A K,2019.Development of a machine vision system using the support vector machine regression (SVR) algorithm for the online prediction of iron ore grades[J].Earth Science Informatics,12(2):197-210.
Ren Zhiwei, Wu Linda,2018.Feature Extraction for hyperspectral images based on principal component analysis improved by information quantity[J].Journal of Sichuan Ordnance,39(7):151-154.
Sun X Y, Li X R, Xiao D, al et,2021.A method of mining truck loading volume detection based on deep learning and image recognition[J].Sensors,21(2):635.
Tian Tian,2011.Coal Petrography Analysis and Coal Pile Volume Measurement Based on Image Processing Technology[D].Taiyuan:Taiyuan University of Technology.
Wang Fang,2020.Research on Primary Selection System of Black Tungsten Ore Based on Machine Vision[D].Ganzhou:Jiangxi University of Science and Technology.
Wang Jun, Pu Lei, He Xinyu, al et,2021.Multiple biological features recognition algorithms of underground coalmine sign-in system[J].Bulletin of Science and Technology,37(3):44-49.
Wang Liguan, Chen Sijia, Jia Mingtao, al et,2020.Beneficiation method of wolframite image recognition based on deep learning[J].The Chinese Journal of Nonferrous Metals,30(5):1192-1201.
Wang Yaoge, Zhang Dongyan, Zhang Ning,2020.Differential element method for definite integrals[J].Studies in College Mathematics,23(6):7-9,20.
Wang Yixin,2016.Application of depth-image processing in vehicle identification[J].Value Engineering,35(25):236-238.
Xiao Jiwei,2019.Research and Design of Black Tungsten Ore Intelligent Sorting System Based on Machine Vision[D].Changsha:Hunan University.
Xu J K, Wang E Y, Zhou R,2020.Real-time measuring and warning of surrounding rock dynamic deformation and failure in deep roadway based on machine vision method[J].Measurement,149:107028.
Yang Boxiong, Yang Yuqi,2019.Applying PCA to dimensionality reduction of image features extracted by deep learning[J].Computer Systems & Applications,28(1):279-283.
Yang Jintao,2019.Research and development of mine electric locomotive[J].World Nonferrous Metals,4(4):234,236.
Yang Wenlong, Ma Baoliang, Chen Chen,2019.A method of measuring the ore carrying capacity of hopper based on depth camera[J].China Tungsten Industry,34(6):69-74.
Ye Zhenglin, Lin Wei,2020.Application of the differential element method in some integral problems[J].Studies in College Mathematics,23(4):32-37.
Zhang Aihua, Tang Tingting, Wang Weiwei, al et,2018.A fast fractal image compression algorithm based on principal component[J]. Computer Technology and Development,28(5):77-80,85.
Zhang Hao, Zhang Qiang, Li Yongxiang, al et,2021.Research on 3D model reconstruction based on deep learning[J].Jou-rnal of Chongqing University of Posts and Telecommunications(Natural Science Edition),33(2):289-295.
Zhang Jian,2017.Application of the differential element method in the volume calculation for the rotating bodies[J].College Mathematics,33(4):104-110.
Zhang Jianli, Ye Pingkun, Sun Shenshen,2020.Detection of ore granularity based on morphological image processing[J].Machinery Design and Manufacture,4(3):68-71.
Zhang Xiaoniu,2016.Design of cleaning car bottom car hydraulic control system in screw type[J].Development & Innovation of Machinery & Electrical Products,29(4):102-103,22.
Zhang Zelin, Yang Jianguo, Su Xiaolan, al et,2013.Estimation of density distribution of coarse coal pile by image analysis[J].Journal of China University of Mining & Technology,42(5):851-858.
Zheng K H, Du C L, Li J P, al et,2015.Underground pneumatic separation of coal and gangue with large size(≥50 mm) in green mining based on the machine vision system[J].Powder Technology,278:223-233.
陈绍杰,李光丽,张伟,等,2011.基于多分类器集成的煤矿区土地利用遥感分类[J].中国矿业大学学报,40(2):273-278.
戴昌璐,2020.XRT射线及图像智能选矿机在黑钨矿山的综合运用探索[J].中国金属通报,(11):187-188.
第旺平,吴志虎,2021.智能光电选矿预选抛废技术研究及应用[J].有色金属(选矿部分),(1):117-121.
杜培军,柳思聪,郑辉,2012.基于支持向量机的矿区土地覆盖变化检测[J].中国矿业大学学报,41(2):262-267.
郭毅,吴富姬,钟毅,等,2021.基于图像识别技术的翻斗式矿车结底智能清理装置研究[J].中国钨业,36(2):76-80.
贺杰,王桂梅,刘杰辉,等,2020.基于图像处理的皮带机上煤量体积计量[J].计量学报,41(12):1516-1520.
李国清,李宝,胡乃联,等,2017.地下金属矿山采掘作业计划优化模型[J].工程科学学报,39(3):342-348.
李和仙,2020.CCD相机的微距测量补光装置设计[J].重庆科技学院学报(自然科学版),22(2):93-96.
李兴东,张睿,2021.关于微元法及其原理的探讨[J].高等数学研究,24(1):80-83.
梁乐,2019.基于双目立体视觉的不规则物体体积测量方法研究[D].西安:西安理工大学.
令晓明,郭锐辛,刘光廷,等,2021.基于PCA降维的多特征级联的行人检测研究[J].制造业自动化,43(3):32-34,76.
任智伟,吴玲达,2018.基于信息量改进主成分分析的高光谱图像特征提取方法[J].兵器装备工程学报,39(7):151-154.
田甜,2011.基于图像处理技术的煤岩分析及煤堆体积测量[D].太原:太原理工大学.
汪钇鑫,2016.深度图像处理在车辆识别中的应用[J].价值工程,35(25):236-238.
王芳,2020.基于机器视觉的黑钨矿石初选系统研究[D].赣州:江西理工大学.
王君,蒲磊,何新宇,等,2021.多生物特征融合的矿井人员身份识别[J].科技通报,37(3):44-49.
王李管,陈斯佳,贾明滔,等,2020.基于深度学习的黑钨矿图像识别选矿方法[J].中国有色金属学报,30(5):1192-1201.
王耀革,张冬燕,张宁,2020.微元分析法探究[J].高等数学研究,23(6):7-9,20.
肖继伟,2019.基于机器视觉的黑钨矿石智能分选系统研究与设计[D].长沙:湖南大学.
杨博雄,杨雨绮,2019.利用PCA进行深度学习图像特征提取后的降维研究[J].计算机系统应用,28(1):279-283.
杨锦涛,2019.矿山工矿电机车研究与发展[J].世界有色金属,4(4):234,236.
杨文龙,马保亮,陈辰,2019.基于深度相机的矿斗载矿量的测量方法[J].中国钨业,34(6):69-74.
叶正麟,林伟,2020.微元法在一些积分问题中的应用[J].高等数学研究,23(4):32-37.
张爱华,唐婷婷,汪玮玮,等,2018.基于主成分特征的快速分形图像压缩算法[J].计算机技术与发展,28(5):77-80,85.
张豪,张强,李勇祥,等,2021.基于深度学习的三维模型重构研究[J].重庆邮电大学学报(自然科学版),33(2):289-295.
张建立,叶平坤,孙深深,2020.形态学图像处理下的矿石粒度的检测[J].机械设计与制造,4(3):68-71.
张健,2017.旋转体体积计算中的微元法思想应用[J].大学数学,33(4):104-110.
张小牛,2016.螺旋式清理矿车结底车液压控制系统的设计[J].机电产品开发与创新,29(4):102-103,22.
张泽琳,杨建国,苏晓兰,等,2013.基于图像分析的粗粒煤堆密度组成估计[J].中国矿业大学学报,42(5):851-858.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] Fengli XIAO,Qingdong ZENG,Fengshan MA,Zhaokun WANG,Zhifu SUN,Zongfeng SUN. Features of Major Gold Metallogenic Fracture Belt in Northwestern Jiaodong[J]. Gold Science and Technology, 2018, 26(4): 396 -405 .
[2] Peng JIN,Kewei LIU,Xudong LI,Jiacai YANG. Numerical Simulation Study of Crack Propagation in Deep Rock Mass Under Water-coupling Blasting[J]. Gold Science and Technology, 2021, 29(1): 108 -119 .
[3] WANG Jiuling,LI Guoqing,HU Nailian,CHEN Boyu. Mining Method Optimization of the Under-Three-Objects Orebody Based on Entropy Weight-CTODIM Evaluation Model[J]. Gold Science and Technology, 2016, 24(4): 81 -86 .
[4] Sihong JIANG, Lili ZHANG, Yifei LIU, Gaofeng LI, Genyuan JI. Distribution Characteristics of Gold Deposits in Africa and Exploration Suggestions[J]. Gold Science and Technology, 2020, 28(4): 465 -478 .
[5] Yuhui WANG,Xianrong LUO,Wang HE,Dong WANG,Guodong TANG,Zhencheng SHANG. Research and Prospecting Prediction of Concealed Tin-Copper Polymetallic Deposit by Geo⁃electrochemical Extraction Method in Jidongwan Mining Area of Luocheng County,Guangxi[J]. Gold Science and Technology, 2021, 29(4): 500 -509 .
[6] Yongchun LIU,Liguan WANG,Jiaxi WU. Optimization of Control Parameters for Underground Load-Haul-Dump Machine Based on LQR-QPSO[J]. Gold Science and Technology, 2021, 29(1): 25 -34 .
[7] Hongke FAN,Genming GUO,Bobo DONG,Kai ZHANG,Yuxin LIU. Characteristics of Rock Geochemical Anomalies and Its Prospecting Effect of the Songshudaban Gold Deposit in Yanqi County,Xinjiang[J]. Gold Science and Technology, 2021, 29(4): 477 -488 .
[8] Shibo YU, Xiaocong YANG, Ye YUAN, Zhixiu WANG. Research on Destress Effect of Ground Pressure Control for the Time-space Mining Sequence at Depths[J]. Gold Science and Technology, 2020, 28(3): 345 -352 .
[9] Lin BI,Liming WANG,Changming DUAN. Research Situation and Development of High-precision Positioning Technology for Underground Mine Environment[J]. Gold Science and Technology, 2021, 29(1): 3 -13 .
[10] Yue JING,Shaofeng WANG,Jintao LU. Thickness Prediction of the Excavation Damage Zone and Non-explosive Mechanized Mining Criterion[J]. Gold Science and Technology, 2021, 29(4): 525 -534 .