Review of deep learning approaches in solving rock fragmentation problems
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 min...
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-08-01
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| Series: | AIMS Mathematics |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/math.20231219?viewType=HTML |
| _version_ | 1797742824427356160 |
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| author | Mikhail V. Ronkin Elena N. Akimova Vladimir E. Misilov |
| author_facet | Mikhail V. Ronkin Elena N. Akimova Vladimir E. Misilov |
| author_sort | Mikhail V. Ronkin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T14:45:49Z |
| format | Article |
| id | doaj.art-5b13ab71a2ec423c83b4ac247fb4d8b8 |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-03-12T14:45:49Z |
| publishDate | 2023-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-5b13ab71a2ec423c83b4ac247fb4d8b82023-08-16T01:52:04ZengAIMS PressAIMS Mathematics2473-69882023-08-01810239002394010.3934/math.20231219Review of deep learning approaches in solving rock fragmentation problemsMikhail V. Ronkin0Elena N. Akimova1Vladimir E. Misilov21. Department of Information Technologies and Control Systems, Institute of Radioelectronics and Information Technology, Ural Federal University, Mira Street 19, 620002 Ekaterinburg, Russia1. Department of Information Technologies and Control Systems, Institute of Radioelectronics and Information Technology, Ural Federal University, Mira Street 19, 620002 Ekaterinburg, Russia 2. Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia1. Department of Information Technologies and Control Systems, Institute of Radioelectronics and Information Technology, Ural Federal University, Mira Street 19, 620002 Ekaterinburg, Russia 2. Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, RussiaOne 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.https://www.aimspress.com/article/doi/10.3934/math.20231219?viewType=HTMLcomputer visiondeep learningconvolutional neural networksrock fragmentationblast quality estimationreal-time performanceparallel computing |
| spellingShingle | Mikhail V. Ronkin Elena N. Akimova Vladimir E. Misilov Review of deep learning approaches in solving rock fragmentation problems AIMS Mathematics computer vision deep learning convolutional neural networks rock fragmentation blast quality estimation real-time performance parallel computing |
| title | Review of deep learning approaches in solving rock fragmentation problems |
| title_full | Review of deep learning approaches in solving rock fragmentation problems |
| title_fullStr | Review of deep learning approaches in solving rock fragmentation problems |
| title_full_unstemmed | Review of deep learning approaches in solving rock fragmentation problems |
| title_short | Review of deep learning approaches in solving rock fragmentation problems |
| title_sort | review of deep learning approaches in solving rock fragmentation problems |
| topic | computer vision deep learning convolutional neural networks rock fragmentation blast quality estimation real-time performance parallel computing |
| url | https://www.aimspress.com/article/doi/10.3934/math.20231219?viewType=HTML |
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