Automatic Asbestos Control Using Deep Learning Based Computer Vision System

The paper discusses the results of the research and development of an innovative deep learning-based computer vision system for the fully automatic asbestos content (productivity) estimation in rock chunk (stone) veins in an open pit and within the time comparable with the work of specialists (about...

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Autores principales:	Vasily Zyuzin, Mikhail Ronkin, Sergey Porshnev, Alexey Kalmykov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning computer vision systems asbestos content control object detection semantic segmentation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/43a306127d5a434ea9de8243c36fd8d2
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id	oai:doaj.org-article:43a306127d5a434ea9de8243c36fd8d2
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spelling	oai:doaj.org-article:43a306127d5a434ea9de8243c36fd8d22021-11-25T16:30:52ZAutomatic Asbestos Control Using Deep Learning Based Computer Vision System10.3390/app1122105322076-3417https://doaj.org/article/43a306127d5a434ea9de8243c36fd8d22021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10532https://doaj.org/toc/2076-3417The paper discusses the results of the research and development of an innovative deep learning-based computer vision system for the fully automatic asbestos content (productivity) estimation in rock chunk (stone) veins in an open pit and within the time comparable with the work of specialists (about 10 min per one open pit processing place). The discussed system is based on the applying of instance and semantic segmentation of artificial neural networks. The Mask R-CNN-based network architecture is applied to the asbestos-containing rock chunks searching images of an open pit. The U-Net-based network architecture is applied to the segmentation of asbestos veins in the images of selected rock chunks. The designed system allows an automatic search and takes images of the asbestos rocks in an open pit in the near-infrared range (NIR) and processes the obtained images. The result of the system work is the average asbestos content (productivity) estimation for each controlled open pit. It is validated to estimate asbestos content as the graduated average ratio of the vein area value to the selected rock chunk area value, both determined by the trained neural network. For both neural network training tasks the training, validation, and test datasets are collected. The designed system demonstrates an error of about 0.4% under different weather conditions in an open pit when the asbestos content is about 1.5–4%. The obtained accuracy is sufficient to use the system as a geological service tool instead of currently applied visual-based estimations.Vasily ZyuzinMikhail RonkinSergey PorshnevAlexey KalmykovMDPI AGarticledeep learningcomputer vision systemsasbestos content controlobject detectionsemantic segmentationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10532, p 10532 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning computer vision systems asbestos content control object detection semantic segmentation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	deep learning computer vision systems asbestos content control object detection semantic segmentation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Vasily Zyuzin Mikhail Ronkin Sergey Porshnev Alexey Kalmykov Automatic Asbestos Control Using Deep Learning Based Computer Vision System
description	The paper discusses the results of the research and development of an innovative deep learning-based computer vision system for the fully automatic asbestos content (productivity) estimation in rock chunk (stone) veins in an open pit and within the time comparable with the work of specialists (about 10 min per one open pit processing place). The discussed system is based on the applying of instance and semantic segmentation of artificial neural networks. The Mask R-CNN-based network architecture is applied to the asbestos-containing rock chunks searching images of an open pit. The U-Net-based network architecture is applied to the segmentation of asbestos veins in the images of selected rock chunks. The designed system allows an automatic search and takes images of the asbestos rocks in an open pit in the near-infrared range (NIR) and processes the obtained images. The result of the system work is the average asbestos content (productivity) estimation for each controlled open pit. It is validated to estimate asbestos content as the graduated average ratio of the vein area value to the selected rock chunk area value, both determined by the trained neural network. For both neural network training tasks the training, validation, and test datasets are collected. The designed system demonstrates an error of about 0.4% under different weather conditions in an open pit when the asbestos content is about 1.5–4%. The obtained accuracy is sufficient to use the system as a geological service tool instead of currently applied visual-based estimations.
format	article
author	Vasily Zyuzin Mikhail Ronkin Sergey Porshnev Alexey Kalmykov
author_facet	Vasily Zyuzin Mikhail Ronkin Sergey Porshnev Alexey Kalmykov
author_sort	Vasily Zyuzin
title	Automatic Asbestos Control Using Deep Learning Based Computer Vision System
title_short	Automatic Asbestos Control Using Deep Learning Based Computer Vision System
title_full	Automatic Asbestos Control Using Deep Learning Based Computer Vision System
title_fullStr	Automatic Asbestos Control Using Deep Learning Based Computer Vision System
title_full_unstemmed	Automatic Asbestos Control Using Deep Learning Based Computer Vision System
title_sort	automatic asbestos control using deep learning based computer vision system
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/43a306127d5a434ea9de8243c36fd8d2
work_keys_str_mv	AT vasilyzyuzin automaticasbestoscontrolusingdeeplearningbasedcomputervisionsystem AT mikhailronkin automaticasbestoscontrolusingdeeplearningbasedcomputervisionsystem AT sergeyporshnev automaticasbestoscontrolusingdeeplearningbasedcomputervisionsystem AT alexeykalmykov automaticasbestoscontrolusingdeeplearningbasedcomputervisionsystem
_version_	1718413132741738496

Automatic Asbestos Control Using Deep Learning Based Computer Vision System

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