Investigation of an object-detection approach for estimating the rock fragmentation in the open-pit conditions
Optimization of open-pit mining is one of significant tasks to date, with the blasting quality estimation being a key factor. The blasting quality is determined through evaluating the number of fragments and block size distribution, the so-called fragmentation task. Currently, computer vision-based...
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| Format: | Article |
| Language: | English |
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Samara National Research University
2024-04-01
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| Series: | Компьютерная оптика |
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| Online Access: | https://www.computeroptics.ru/eng/KO/Annot/KO48-2/480214e.html |
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| author | K. Reshetnikov M. Ronkin S. Porshnev |
| author_facet | K. Reshetnikov M. Ronkin S. Porshnev |
| author_sort | K. Reshetnikov |
| collection | DOAJ |
| description | Optimization of open-pit mining is one of significant tasks to date, with the blasting quality estimation being a key factor. The blasting quality is determined through evaluating the number of fragments and block size distribution, the so-called fragmentation task. Currently, computer vision-based methods using instance or semantic segmentation approaches are most widely applied in the task. However, in practice, such approaches require a lot of computational resources. Because of this, the use of alternative techniques based on algorithms for the real-time object detection is highly relevant. The paper studies the use of YOLO family architectures for solving the task of the blasting quality assessment. Based on the research results, YOLOv7x architecture is proposed as a baseline model. The proposed neural network architecture was trained on a dataset selected by the present authors from digital images of blasted open-pit block fragments, which consisted of 220 images. The obtained results also allow one to suggest the geometrical size of rock chunks as a measure of blasting quality. |
| format | Article |
| id | doaj-art-e6336a86bba14312b96348696ccab4b7 |
| institution | Kabale University |
| issn | 0134-2452 2412-6179 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj-art-e6336a86bba14312b96348696ccab4b72025-02-04T12:51:46ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792024-04-0148227228110.18287/2412-6179-CO-1382Investigation of an object-detection approach for estimating the rock fragmentation in the open-pit conditionsK. Reshetnikov0M. Ronkin 1S. Porshnev2Federal State Autonomous Educational Institution of Higher Education «Ural Federal University named after the first President of Russia B.N.Yeltsin»Federal State Autonomous Educational Institution of Higher Education «Ural Federal University named after the first President of Russia B.N.Yeltsin»Federal State Autonomous Educational Institution of Higher Education «Ural Federal University named after the first President of Russia B.N.Yeltsin»Optimization of open-pit mining is one of significant tasks to date, with the blasting quality estimation being a key factor. The blasting quality is determined through evaluating the number of fragments and block size distribution, the so-called fragmentation task. Currently, computer vision-based methods using instance or semantic segmentation approaches are most widely applied in the task. However, in practice, such approaches require a lot of computational resources. Because of this, the use of alternative techniques based on algorithms for the real-time object detection is highly relevant. The paper studies the use of YOLO family architectures for solving the task of the blasting quality assessment. Based on the research results, YOLOv7x architecture is proposed as a baseline model. The proposed neural network architecture was trained on a dataset selected by the present authors from digital images of blasted open-pit block fragments, which consisted of 220 images. The obtained results also allow one to suggest the geometrical size of rock chunks as a measure of blasting quality.https://www.computeroptics.ru/eng/KO/Annot/KO48-2/480214e.htmlfragmentationdeep learningobject detectioncomputer visionopen-pitblast quality estimation |
| spellingShingle | K. Reshetnikov M. Ronkin S. Porshnev Investigation of an object-detection approach for estimating the rock fragmentation in the open-pit conditions Компьютерная оптика fragmentation deep learning object detection computer vision open-pit blast quality estimation |
| title | Investigation of an object-detection approach for estimating the rock fragmentation in the open-pit conditions |
| title_full | Investigation of an object-detection approach for estimating the rock fragmentation in the open-pit conditions |
| title_fullStr | Investigation of an object-detection approach for estimating the rock fragmentation in the open-pit conditions |
| title_full_unstemmed | Investigation of an object-detection approach for estimating the rock fragmentation in the open-pit conditions |
| title_short | Investigation of an object-detection approach for estimating the rock fragmentation in the open-pit conditions |
| title_sort | investigation of an object detection approach for estimating the rock fragmentation in the open pit conditions |
| topic | fragmentation deep learning object detection computer vision open-pit blast quality estimation |
| url | https://www.computeroptics.ru/eng/KO/Annot/KO48-2/480214e.html |
| work_keys_str_mv | AT kreshetnikov investigationofanobjectdetectionapproachforestimatingtherockfragmentationintheopenpitconditions AT mronkin investigationofanobjectdetectionapproachforestimatingtherockfragmentationintheopenpitconditions AT sporshnev investigationofanobjectdetectionapproachforestimatingtherockfragmentationintheopenpitconditions |