Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/102838
Title: Computer vision system for the automatic asbestos content control in stones
Authors: Zyuzin, V.
Ronkin, M.
Porshnev, S.
Kalmykov, A.
Issue Date: 2021
Publisher: IOP Publishing Ltd
Citation: Computer vision system for the automatic asbestos content control in stones / V. Zyuzin, M. Ronkin, S. Porshnev, et al. — DOI 10.1088/1742-6596/1727/1/012014 // Journal of Physics: Conference Series. — 2021. — Vol. 1727. — Iss. 1. — 012014.
Abstract: The paper discusses the results of the first stage of research and development an innovative computer vision system for the automatic asbestos content control in stones veins at an asbestos processing factory. The discussed system is based on the applying of a semantic segmentation artificial neural networks, in particular U-Net based network architectures for solving both: the boundariesof stones segmentation and veins inside them. At the current stage, the following tasks were solved. 1. The discussed system prototype is developed. The system is allowing to takes images of the asbestos stones on the conveyor belt in the near-infrared range (NIR), avoiding the outer lighting influence, and processing the obtaining images. 2. The training, validation and test datasets were collected. 3. Substantiated the choice of the U-Net based neural network. 4. Proposed to estimate the resulted specific asbestos concentration as the average relation of all the veins square to all stones square on the image. 5. The resulted deviation between obtained and laboratory given results ofthe asbestos concentration is about 0.058 in the slope of graduation curve. The farther improvementrecommendations for the developed system are given. © Published under licence by IOP Publishing Ltd.
Keywords: BELT CONVEYORS
BIG DATA
COMPUTER CONTROL SYSTEMS
COMPUTER VISION
INFRARED DEVICES
NETWORK ARCHITECTURE
NEURAL NETWORKS
SEMANTICS
COMPUTER VISION SYSTEM
CONTENT CONTROL
CONVEYOR BELTS
NEAR-INFRARED RANGE
RESEARCH AND DEVELOPMENT
SEMANTIC SEGMENTATION
SYSTEM PROTOTYPE
ASBESTOS
URI: http://hdl.handle.net/10995/102838
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85101718585
PURE ID: 21022089
e538f550-de64-4d93-8875-2404059acc81
ISSN: 17426588
DOI: 10.1088/1742-6596/1727/1/012014
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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