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Asbestos Veins Instance Segmentation in the Open-Pits. / Ronkin, Mikhail; Misilov, Vladimir; Akimova, Elena et al.
Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. p. 247-250.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Ronkin, M, Misilov, V, Akimova, E & Miftakhov, V 2023, Asbestos Veins Instance Segmentation in the Open-Pits. in Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., pp. 247-250, 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), Екатеринбург, Russian Federation, 15/05/2023. https://doi.org/10.1109/USBEREIT58508.2023.10158814

APA

Ronkin, M., Misilov, V., Akimova, E., & Miftakhov, V. (2023). Asbestos Veins Instance Segmentation in the Open-Pits. In Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book (pp. 247-250). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/USBEREIT58508.2023.10158814

Vancouver

Ronkin M, Misilov V, Akimova E, Miftakhov V. Asbestos Veins Instance Segmentation in the Open-Pits. In Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc. 2023. p. 247-250 doi: 10.1109/USBEREIT58508.2023.10158814

Author

Ronkin, Mikhail ; Misilov, Vladimir ; Akimova, Elena et al. / Asbestos Veins Instance Segmentation in the Open-Pits. Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023: book. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 247-250

BibTeX

@inproceedings{33c2c6f1803f48e1aeb3d44917874a64,
title = "Asbestos Veins Instance Segmentation in the Open-Pits",
abstract = "The paper discusses the results of the investigation of the rock chunks vein segmentation task using deep-learning computer vision systems. The previously collected and labeled database of asbestos veins in the rock chunks of the Open Pit is under study. The task has practical novelty, and allows carrying out real-time-scale automatic productivity estimation of the open pit. From the reseapoint of of view field, to the best of our knowledge, the task is mostly related to the fragmentation one. However, it is shown that only base-line solutions (using deep learning) here have been researched. Most of those solutions using either a semantic segmentation approach with further instances division or strictly instance segmentation approach. The current paper investigate the last one comparing several Mask-R-CNN architectures. The usage of the methods under research in the described area draw up the novelty of the study. The current results provides the base-line solution for the next researches in this field.",
author = "Mikhail Ronkin and Vladimir Misilov and Elena Akimova and Valery Miftakhov",
year = "2023",
month = may,
day = "15",
doi = "10.1109/USBEREIT58508.2023.10158814",
language = "English",
pages = "247--250",
booktitle = "Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) ; Conference date: 15-05-2023 Through 17-05-2023",

}

RIS

TY - GEN

T1 - Asbestos Veins Instance Segmentation in the Open-Pits

AU - Ronkin, Mikhail

AU - Misilov, Vladimir

AU - Akimova, Elena

AU - Miftakhov, Valery

PY - 2023/5/15

Y1 - 2023/5/15

N2 - The paper discusses the results of the investigation of the rock chunks vein segmentation task using deep-learning computer vision systems. The previously collected and labeled database of asbestos veins in the rock chunks of the Open Pit is under study. The task has practical novelty, and allows carrying out real-time-scale automatic productivity estimation of the open pit. From the reseapoint of of view field, to the best of our knowledge, the task is mostly related to the fragmentation one. However, it is shown that only base-line solutions (using deep learning) here have been researched. Most of those solutions using either a semantic segmentation approach with further instances division or strictly instance segmentation approach. The current paper investigate the last one comparing several Mask-R-CNN architectures. The usage of the methods under research in the described area draw up the novelty of the study. The current results provides the base-line solution for the next researches in this field.

AB - The paper discusses the results of the investigation of the rock chunks vein segmentation task using deep-learning computer vision systems. The previously collected and labeled database of asbestos veins in the rock chunks of the Open Pit is under study. The task has practical novelty, and allows carrying out real-time-scale automatic productivity estimation of the open pit. From the reseapoint of of view field, to the best of our knowledge, the task is mostly related to the fragmentation one. However, it is shown that only base-line solutions (using deep learning) here have been researched. Most of those solutions using either a semantic segmentation approach with further instances division or strictly instance segmentation approach. The current paper investigate the last one comparing several Mask-R-CNN architectures. The usage of the methods under research in the described area draw up the novelty of the study. The current results provides the base-line solution for the next researches in this field.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85164920643

U2 - 10.1109/USBEREIT58508.2023.10158814

DO - 10.1109/USBEREIT58508.2023.10158814

M3 - Conference contribution

SP - 247

EP - 250

BT - Proceedings - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)

Y2 - 15 May 2023 through 17 May 2023

ER -

ID: 41994543