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Gold Science and Technology >

2021 , Vol. 29 >Issue 1: 136 - 146

DOI: https://doi.org/10.11872/j.issn.1005-2518.2021.01.089

Mining Technology and Mine Management

Obstacle Detection Technology of Mine Electric Locomotive Driverless Based on Computer Vision Technology

  • Jinghua WANG ,
  • Liguan WANG ,
  • Lin Bi
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  • School of Resources and Safety Engineering，Central South University，Changsha 410083，Hunan，China

Received date: 2020-05-18

  Revised date: 2020-08-03

  Online published: 2021-03-22

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Highlights

The mining of mineral resources is getting deeper and deeper，the working environment is bad，the employees are aging seriously，and the human cost is rising，which brings great challenges to the development of mining industry.Intelligent mine operation has become an inevitable trend.As a part of mine intellec-tualization，unmanned transportation system is very important for mine，which means the improvement of safety production efficiency can achieve zero injury and zero time loss.Considering the development demand of driverless electric vehicle and the traditional computer vision method，it is difficult to realize the real-time detection and location of obstacles.An intelligent obstacle detection method based on the combination of traditional computer vision technology and deep learning target detection algorithm YOLOv3 was proposed.First of all，the video data of obstacles in the driving area of electric locomotive（this paper calls it effective detection area） were collected，using an image annotation tool namely labelimg to make VOC data set，using YOLOv3 to train data set，according to the feedback results，adjust the parameters continuously to obtain the relative optimal parameters，and finally get the obstacle detection model.Then use the traditional computer vision technology to locate the track by edge，texture and other information，using the “3 neighborhood” search method to get the track line coordinate value of left and right track lines，and expand a certain distance to the outside of the track according to the distance information，extracting the effective detection area，thus reducing the computational complexity of the later obstacle detection，at the same time，gridding images，converting the coordinates of obstacles to the actual distance.Finally，using the obstacle detection model to detect the effective detection area and respond to the detection results.Experimental results show that the method can identify many objects with different characteristics in the driving area，such as electric locomotive，people，large falling rocks，etc.It can process 6 pictures per second，and the average accuracy of the actual data collected in the field reaches 93.2%，it has good performance in real-time and accuracy，and has a good effect in the underground mine scene.

Cite this article

Jinghua WANG , Liguan WANG , Lin Bi . Obstacle Detection Technology of Mine Electric Locomotive Driverless Based on Computer Vision Technology[J]. Gold Science and Technology, 2021 , 29(1) : 136 -146 . DOI: 10.11872/j.issn.1005-2518.2021.01.089

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哈莫尼金矿公司正考虑深掘世界最深的姆波能金矿

据彭博新闻社（Bloomberg News）2021年2月24日报道，哈莫尼金矿公司（Harmony Gold Mining Co.）正考虑在世界最深的金矿进一步加大地下开采深度，因为南非生产商发现，挖掘日益减少的矿石储量变得愈加困难。

哈莫尼公司首席执行官Peter Steenkamp表示，公司正在研究在姆波能（Mponeng）开采超过目前4 km深度的金矿，有可能将矿山寿命延长20~30年。他认为，超过这一深度以下部位的矿石储量“巨大”，哈莫尼公司正在探索开发这些矿藏所需的方法和投资。

哈莫尼金矿公司是南非仅存的几家从老化资产中榨取利润的黄金生产商之一，由黑人亿万富翁Patrice Motsepe旗下非洲彩虹矿业有限公司（African Rainbow Minerals Ltd.）所支持，去年从安格鲁金矿公司（AngloGold Ashanti Ltd.）手中收购了姆波能金矿及其资产，成为南非第一大黄金生产商。

哈莫尼公司近期公布上半年第一阶段利润增长逾3倍。该公司的目标是将姆波能金矿的年产量维持在25万盎司（7吨）左右，这可能有助于将公司的总产量维持在160万盎司（45.36 t）左右。然而，随着开采深度的增加，地震事件和工人被困井下死亡的风险也在增加。该公司表示，2020年6月至12月之间，在公司运营过程中发生的矿难事故造成6名工人死亡。

姆波能世界级金矿是目前世界上最深的矿井，也是规模最大、品位最高的金矿山之一。矿山位于南非西北省，维特瓦特斯兰德盆地（Witwatersrand Basin）的西北缘，属于兰德式（Rand-type）古砾岩型金—铀矿床。截止2019年12月，姆波能金矿已探明和潜在矿石储量约3 619万吨，黄金品位9.54 g/t，所含黄金储量约1 100万盎司（345 t）；2019年姆波能金矿的黄金产量为22.4万盎司（6.92 t）。

南非黄金行业一度是全球最大的，但随着开采深层金矿成本的增加和地质难度的增加，该国的黄金行业已出现萎缩。随着安格鲁金矿公司和金田公司（Gold Fields Ltd.）等大型黄金生产商将重点转移到非洲、澳大利亚和美洲其他利润丰厚的矿藏上，南非黄金行业2020年生产了91 t黄金，目前只有9.3万名员工。

（来源：中国矿业网）

http://www.goldsci.ac.cn/article/2021/1005-2518/1005-2518-2021-29-1-136.shtml

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