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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

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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
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