Geodesic
Review on application of deep neural networks and parallel architectures for rock fragmentation problems
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 12 (2023) no. 4, pp. 5-54

Voir la notice de l'article provenant de la source Math-Net.Ru

Evaluation of mining productivity, including the determination of the geometric dimensions of rock objects in an open pit, is one of the most critical tasks in the mining industry. The problem of rock fragmentation is usually solved using computer vision methods such as instance segmentation or semantic segmentation. Today, deep learning neural networks are used to solve such problems for digital images. Neural networks require a lot of computing power to process high-resolution digital images and large datasets. To address this issue, in literature, lightweight architectural neural networks are proposed, as well as parallel computing using CPU, GPU, and specialized accelerators. The review discusses the latest advances in the field of deep learning neural networks for solving computer vision problems in relation to rock fragmentation and aspects of improving the performance of neural network implementations on various parallel architectures.
Keywords: computer vision, convolutional neural networks, deep learning, semantic segmentation, object detection, parallel computing, mining industry problems, rock fragmentation.
Mots-clés : instance segmentation 
@article{VYURV_2023_12_4_a0,
     author = {M. V. Ronkin and E. N. Akimova and V. E. Misilov and K. I. Reshetnikov},
     title = {Review on application of deep neural networks and parallel architectures for rock fragmentation problems},
     journal = {Vestnik \^U\v{z}no-Uralʹskogo gosudarstvennogo universiteta. Seri\^a Vy\v{c}islitelʹna\^a matematika i informatika},
     pages = {5--54},
     publisher = {mathdoc},
     volume = {12},
     number = {4},
     year = {2023},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VYURV_2023_12_4_a0/}
}
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M. V. Ronkin; E. N. Akimova; V. E. Misilov; K. I. Reshetnikov. Review on application of deep neural networks and parallel architectures for rock fragmentation problems. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, Tome 12 (2023) no. 4, pp. 5-54. http://geodesic.mathdoc.fr/item/VYURV_2023_12_4_a0/