IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i7p3051-d1623794.html
   My bibliography  Save this article

A Review of the Application of Computer Vision Techniques in Sustainable Engineering of Open Pit Mines

Author

Listed:
  • Di Shan

    (Institute of Minerals Research, University of Science and Technology Beijing, Beijing 100083, China
    School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
    China Mineral Resources Group Co., Ltd., Baoding 070001, China)

  • Fuming Qu

    (Institute of Minerals Research, University of Science and Technology Beijing, Beijing 100083, China)

  • Zheng Wang

    (Ansteel Mining Co., Ltd., Anshan 114001, China)

  • Yaming Ji

    (Institute of Minerals Research, University of Science and Technology Beijing, Beijing 100083, China
    School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Jianwei Xu

    (Institute of Minerals Research, University of Science and Technology Beijing, Beijing 100083, China
    The School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China)

Abstract

Mineral resources are important industrial raw materials and the cornerstone of ensuring industrial production, especially metal ores. With the continuous development and progress of artificial intelligence technology, it is of great significance to apply artificial intelligence technology to mining. Computer vision technology, as a sensor that collects information like a human “eye”, is becoming increasingly important in ensuring mining safety, improving mining continuity, and reducing environmental interference through computer vision methods. In this context, this paper focuses on general problems of metal mineral resources, the sustainability of exploration, drilling and blasting, transport, personnel safety, and security. It describes the latest progress of computer vision technology in each link and summarizes and looks forward to the key technical methods. It also summarizes and looks ahead to the key technical methods in each area. The research results show that the application of computer-vision-related technologies in related links not only greatly improves production efficiency but also reduces environmental interference and the probability of production safety accidents, effectively ensuring sustainable mining. In the future, to achieve unmanned mining throughout the entire process, it will be necessary to combine computer vision technology with other specialties such as intelligent control and intelligent perception to achieve a technological breakthrough throughout the entire process.

Suggested Citation

  • Di Shan & Fuming Qu & Zheng Wang & Yaming Ji & Jianwei Xu, 2025. "A Review of the Application of Computer Vision Techniques in Sustainable Engineering of Open Pit Mines," Sustainability, MDPI, vol. 17(7), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3051-:d:1623794
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/7/3051/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/7/3051/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kexue Zhang & Lei Kang & Xuexi Chen & Manchao He & Chun Zhu & Dong Li, 2022. "A Review of Intelligent Unmanned Mining Current Situation and Development Trend," Energies, MDPI, vol. 15(2), pages 1-19, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexey Y. Bykovsky & Nikolay A. Vasiliev, 2023. "Parametrical T -Gate for Joint Processing of Quantum and Classic Optoelectronic Signals," J, MDPI, vol. 6(3), pages 1-27, July.
    2. Kexue Zhang & Junao Zhu & Manchao He & Yaodong Jiang & Chun Zhu & Dong Li & Lei Kang & Jiandong Sun & Zhiheng Chen & Xiaoling Wang & Haijiang Yang & Yongwei Wu & Xingcheng Yan, 2022. "Research on Intelligent Comprehensive Evaluation of Coal Seam Impact Risk Based on BP Neural Network Model," Energies, MDPI, vol. 15(9), pages 1-14, April.
    3. Zhang, Yan & Wang, Yu-Hao & Zhao, Xu & Tong, Rui-Peng, 2023. "Dynamic probabilistic risk assessment of emergency response for intelligent coal mining face system, case study: Gas overrun scenario," Resources Policy, Elsevier, vol. 85(PB).
    4. Zhang, Yan & Sun, Ninghao & Hu, Xiangyang & Tong, Ruipeng, 2024. "Developing the human reliability analysis model tailored for intelligent coal mining face system," Resources Policy, Elsevier, vol. 98(C).
    5. Shuai Li & Lifeng Yu & Wanjun Jiang & Haoxuan Yu & Xinmin Wang, 2022. "The Recent Progress China Has Made in Green Mine Construction, Part I: Mining Groundwater Pollution and Sustainable Mining," IJERPH, MDPI, vol. 19(9), pages 1-19, May.
    6. Xing, Zhizhong & Zhao, Shuanfeng & Guo, Wei & Meng, Fanyuan & Guo, Xiaojun & Wang, Shenquan & He, Haitao, 2023. "Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model," Energy, Elsevier, vol. 285(C).
    7. Feng, Juqiang & Cai, Feng & Zhao, Yang & Zhang, Xing & Zhan, Xinju & Wang, Shunli, 2024. "A novel feature optimization and ensemble learning method for state-of-health prediction of mining lithium-ion batteries," Energy, Elsevier, vol. 299(C).
    8. Fangtian Wang & Hongfei Qu & Wei Tian & Shilei Zhai & Liqiang Ma, 2022. "Ethical Construction and Development of Mining Engineering Based on the Safe, Efficient, Green, and Low-Carbon Concept," Sustainability, MDPI, vol. 14(21), pages 1-14, October.
    9. Lin He & Dongliang Yuan & Lianwei Ren & Ming Huang & Wenyu Zhang & Jie Tan, 2023. "Evaluation Model Research of Coal Mine Intelligent Construction Based on FDEMATEL-ANP," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    10. Xue Yang & Qiyu Xing & Juan Yang & Yujia Yao & Kang Tian & Hao Cai, 2024. "Research on Influencing Factors and Mechanisms of Human-Machine Safety Collaboration Behavior in Coal Mines Based on DEMATEL-ISM," SAGE Open, , vol. 14(1), pages 21582440241, March.
    11. Olga Zhironkina & Sergey Zhironkin, 2023. "Technological and Intellectual Transition to Mining 4.0: A Review," Energies, MDPI, vol. 16(3), pages 1-37, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3051-:d:1623794. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.