Abstract—
This work is devoted to the selection and configuration of a convolutional neural network for the recognition of unsuitable marble for processing on a conveyor belt, as well as the creation of an application for the issuance of coordinates of unsuitable rock for removal from the belt.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
REFERENCES
“Supeshala Chamidu YOLO v4 or YOLO v5 or PP-YOLO?,” Towards Data Sci. https://towardsdatascience. com/yolo-v4-or-yolo-v5-or-pp-yolo-dad8e40f7109. Cited March 23, 2022.
P. Flach, Machine Learning: The Art and Science of Algorithms That Make Sense of Data (Cambridge Univ. Press, 2012; DMK Press, Moscow, 2015).
“Matthijs Hollemans MobileNet version 2,” MachineThink. https://machinethink.net/blog/mobilenet-v2/. Cited March 14, 2022.
U. Nepal and H. Eslamiat, “Comparing YOLOv3, YOLOv4, and YOLOv5 for autonomous landing spot detection in faulty UAVs,” Sensors 22, 464 (2022). https://doi.org/10.3390/s22020464
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
The authors declare that they have no conflicts of interest.
Additional information
Andrei Sergeevich Mokhov. In 2012, he completed the Master’s program at the Moscow Power Engineering Institute; in 2017, he defended his dissertation on machine learning (majoring in 05.13.01). He works at the Moscow Power Engineering Institute as an Assistant Professor of the Department of Management and Intelligent Technologies, and teaches courses on data analysis and software development. About 40 scientific articles were published, one certificate for the computer program.
D.A. Shestov. In 2010, he graduated from the Moscow Power Engineering Institute (Technical University) majoring in Automation and Control, was trained in postgraduate studies at the All-Russian Scientific Research Institute of Rural Electrification majoring in 05.20.02—Electrical Technology and Electrical Equipment in Agriculture. On September 22, 2020, he defended his dissertation for the degree of Candidate of engineering sciences in the specialty 05.20.02. Since March 2015, he has been working as the head of the educational laboratory at the Moscow Power Engineering Institute in the Department of Management and Intellectual Technologies. Certified participant of the WorldSkills competition in the competence of “mechatronics” as an expert. He is the winner of the programs of the Foundation for Assistance to Small Innovative Enterprises in Science and Technology (Assistance Foundation) U.M.N.I.K., START-1, START-2, and START-3. Published about 40 scientific articles, has seven patents for inventions and four certificates for the computer program.
Ivan Vyacheslavovich Shubin. In 2022, he completed his studies for the Bachelor’s program “Management in Technical Systems” at the Moscow Power Engineering Institute; since September 2022, he has been studying in the Master’s degree program of the Moscow Power Engineering Institute.
Rights and permissions
About this article
Cite this article
Mokhov, A.S., Shestov, D.A. & Shubin, I.V. Development of a System for Detecting an Unsuitable Marble Stone by Using Convolutional Neural Networks. Pattern Recognit. Image Anal. 33, 413–416 (2023). https://doi.org/10.1134/S1054661823030306
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S1054661823030306