Review of deep learning approaches in solving rock fragmentation problems
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Review of deep learning approaches in solving rock fragmentation problems
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| Автор |
Ronkin, M. V.
Akimova, E. N. Misilov, V. E. |
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| Тематика |
BLAST QUALITY ESTIMATION
COMPUTER VISION CONVOLUTIONAL NEURAL NETWORKS DEEP LEARNING PARALLEL COMPUTING REAL-TIME PERFORMANCE ROCK FRAGMENTATION |
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| Описание |
One of the most significant challenges of the mining industry is resource yield estimation from visual data. An example would be identification of the rock chunk distribution parameters in an open pit. Solution of this task allows one to estimate blasting quality and other parameters of open-pit mining. This task is of the utmost importance, as it is critical to achieving optimal operational efficiency, reducing costs and maximizing profits in the mining industry. The mentioned task is known as rock fragmentation estimation and is typically tackled using computer vision techniques like instance segmentation or semantic segmentation. These problems are often solved using deep learning convolutional neural networks. One of the key requirements for an industrial application is often the need for real-time operation. Fast computation and accurate results are required for practical tasks. Thus, the efficient utilization of computing power to process high-resolution images and large datasets is essential. Our survey is focused on the recent advancements in rock fragmentation, blast quality estimation, particle size distribution estimation and other related tasks. We consider most of the recent results in this field applied to open-pit, conveyor belts and other types of work conditions. Most of the reviewed papers cover the period of 2018-2023. However, the most significant of the older publications are also considered. A review of publications reveals their specificity, promising trends and best practices in this field. To place the rock fragmentation problems in a broader context and propose future research topics, we also discuss state-of-the-art achievements in real-time computer vision and parallel implementations of neural networks. © 2023 the Author(s), licensee AIMS Press.
Russian Science Foundation, RSF: 22-21-20051 This research was supported by the Russian Science Foundation and Government of Sverdlovsk region, Joint Grant No 22-21-20051, https://rscf.ru/en/project/22-21-20051/. |
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| Дата |
2024-04-05T16:30:46Z
2024-04-05T16:30:46Z 2023 |
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| Тип |
Review
Review (info:eu-repo/semantics/review) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Ronkin, M, Akimova, E & Misilov, V 2023, 'Review of deep learning approaches in solving rock fragmentation problems', Aims mathematics, Том. 8, № 10, стр. 23900-23940. https://doi.org/10.3934/math.20231219
Ronkin, M., Akimova, E., & Misilov, V. (2023). Review of deep learning approaches in solving rock fragmentation problems. Aims mathematics, 8(10), 23900-23940. https://doi.org/10.3934/math.20231219 2473-6988 Final All Open Access, Gold https://www.scopus.com/inward/record.uri?eid=2-s2.0-85167436190&doi=10.3934%2fmath.20231219&partnerID=40&md5=6eb97da648085763f253880a0150698b https://doi.org/10.3934/math.20231219 http://elar.urfu.ru/handle/10995/130706 10.3934/math.20231219 85167436190 001052388300024 |
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| Язык |
en
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| Связанные ресурсы |
info:eu-repo/grantAgreement/RSF//22-21-20051
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
American Institute of Mathematical Sciences
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| Источник |
AIMS Mathematics
AIMS Mathematics |
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