Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet

Bench blasting is the primary means of production in open-pit metal mines. The size of the resulting rock mass after blasting has a significant impact on production cost. Currently, the ore fragment size is obtained mainly through manual measurement or estimation with the naked eye, which is ineffic...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:PloS one 2023-09, Vol.18 (9), p.e0291115
Hauptverfasser: Jin, Changyu, Liang, Junyu, Fan, Chunhui, Chen, Lijun, Wang, Qiang, Lu, Yu, Wang, Kai
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 9
container_start_page e0291115
container_title PloS one
container_volume 18
creator Jin, Changyu
Liang, Junyu
Fan, Chunhui
Chen, Lijun
Wang, Qiang
Lu, Yu
Wang, Kai
description Bench blasting is the primary means of production in open-pit metal mines. The size of the resulting rock mass after blasting has a significant impact on production cost. Currently, the ore fragment size is obtained mainly through manual measurement or estimation with the naked eye, which is inefficient and inaccurate. This study proposes the U-CARFnet and U-Net models for segmenting blasting fragment images from open-pit mines based on an attention mechanism, residual learning module, and focal loss function. It compares this technique with traditional image segmentation ones and a variety of deep learning models to verify the efficacy of the proposed model. Experimental results show that the accuracy of the U-CARFnet model proposed in this paper reaches 97.11% in the performance evaluation, which shows better performance than the traditional image segmentation method. In this study, the U-CARFnet model is used in the application, and a superior performance is obtained, with an average segmentation error of 5.46%. The proposed approach provides an effective technique for statistically analyzing images of mine rock.
doi_str_mv 10.1371/journal.pone.0291115
format Article
fullrecord <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2864887678</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A765314784</galeid><doaj_id>oai_doaj_org_article_c1ec382e4d204fdbae81557a0c037a4a</doaj_id><sourcerecordid>A765314784</sourcerecordid><originalsourceid>FETCH-LOGICAL-c619t-d998e7746750c7399e63da762b4d87796008b62c50278cedd456cdab33ecff043</originalsourceid><addsrcrecordid>eNqNkl-L1DAUxYsouK5-A8GCIOtDx6Rpk_RJhsHVgYWFXVcfQ5rcdjK0ydik4n5705kqW9kHyUP-_e5J7uEkyWuMVpgw_GHvxsHKbnVwFlYorzDG5ZPkDFckz2iOyNMH6-fJC-_3CJWEU3qWfL8No75PnU09tD3YIIOJG9ekdSd9MLZNm0Eeb1LTyxZ83Ls-dQew2cGEtDcW0lp60JPIXbZZ31xaCC-TZ43sPLya5_Pk7vLT182X7Or683azvsoUxVXIdFVxYKygrESKkaoCSrRkNK8LzRmrKEK8prkqUc64Aq2Lkiota0JANQ0qyHny5qR76JwXsxFe5JwWnDPKeCS2J0I7uReHIXYx3AsnjTgeuKEVcghGdSAUBkV4DoXOUdHoWgLHZckkUogwWcio9XF-bax70Cq6MshuIbq8sWYnWvdTYFQiHJuMChezwuB-jOCD6I1X0HXSghuPHy8ZzzGZ0Lf_oI-3N1OtjB0Y27j4sJpExZrRkuCC8cmm1SNUHBp6o2JsGhPPFwXvFwWRCfArtHL0Xmxvb_6fvf62ZN89YHcgu7Dzrhun1PklWJxANTjvB2j-uoyRmFL_xw0xpV7MqSe_AaiK868</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2864887678</pqid></control><display><type>article</type><title>Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet</title><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Jin, Changyu ; Liang, Junyu ; Fan, Chunhui ; Chen, Lijun ; Wang, Qiang ; Lu, Yu ; Wang, Kai</creator><contributor>Peña, Mª Ángeles</contributor><creatorcontrib>Jin, Changyu ; Liang, Junyu ; Fan, Chunhui ; Chen, Lijun ; Wang, Qiang ; Lu, Yu ; Wang, Kai ; Peña, Mª Ángeles</creatorcontrib><description>Bench blasting is the primary means of production in open-pit metal mines. The size of the resulting rock mass after blasting has a significant impact on production cost. Currently, the ore fragment size is obtained mainly through manual measurement or estimation with the naked eye, which is inefficient and inaccurate. This study proposes the U-CARFnet and U-Net models for segmenting blasting fragment images from open-pit mines based on an attention mechanism, residual learning module, and focal loss function. It compares this technique with traditional image segmentation ones and a variety of deep learning models to verify the efficacy of the proposed model. Experimental results show that the accuracy of the U-CARFnet model proposed in this paper reaches 97.11% in the performance evaluation, which shows better performance than the traditional image segmentation method. In this study, the U-CARFnet model is used in the application, and a superior performance is obtained, with an average segmentation error of 5.46%. The proposed approach provides an effective technique for statistically analyzing images of mine rock.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0291115</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Biology and Life Sciences ; Blasting ; Computer and Information Sciences ; Datasets ; Deep learning ; Engineering and Technology ; Image processing ; Image segmentation ; Iron compounds ; Management ; Medical imaging equipment ; Microscopy ; Mineral industry ; Mines ; Mining ; Mining engineering ; Mining industry ; Modelling ; Molybdenum ; Neural networks ; Ores ; Particle size ; Performance evaluation ; Production costs ; Production management ; Research and Analysis Methods ; Rock masses ; Rocks ; Semantics ; Social Sciences ; Statistical analysis</subject><ispartof>PloS one, 2023-09, Vol.18 (9), p.e0291115</ispartof><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Jin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Copyright: © 2023 Jin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>2023 Jin et al 2023 Jin et al</rights><rights>2023 Jin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c619t-d998e7746750c7399e63da762b4d87796008b62c50278cedd456cdab33ecff043</cites><orcidid>0000-0001-7656-4644</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501675/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501675/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids></links><search><contributor>Peña, Mª Ángeles</contributor><creatorcontrib>Jin, Changyu</creatorcontrib><creatorcontrib>Liang, Junyu</creatorcontrib><creatorcontrib>Fan, Chunhui</creatorcontrib><creatorcontrib>Chen, Lijun</creatorcontrib><creatorcontrib>Wang, Qiang</creatorcontrib><creatorcontrib>Lu, Yu</creatorcontrib><creatorcontrib>Wang, Kai</creatorcontrib><title>Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet</title><title>PloS one</title><description>Bench blasting is the primary means of production in open-pit metal mines. The size of the resulting rock mass after blasting has a significant impact on production cost. Currently, the ore fragment size is obtained mainly through manual measurement or estimation with the naked eye, which is inefficient and inaccurate. This study proposes the U-CARFnet and U-Net models for segmenting blasting fragment images from open-pit mines based on an attention mechanism, residual learning module, and focal loss function. It compares this technique with traditional image segmentation ones and a variety of deep learning models to verify the efficacy of the proposed model. Experimental results show that the accuracy of the U-CARFnet model proposed in this paper reaches 97.11% in the performance evaluation, which shows better performance than the traditional image segmentation method. In this study, the U-CARFnet model is used in the application, and a superior performance is obtained, with an average segmentation error of 5.46%. The proposed approach provides an effective technique for statistically analyzing images of mine rock.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Blasting</subject><subject>Computer and Information Sciences</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Engineering and Technology</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>Iron compounds</subject><subject>Management</subject><subject>Medical imaging equipment</subject><subject>Microscopy</subject><subject>Mineral industry</subject><subject>Mines</subject><subject>Mining</subject><subject>Mining engineering</subject><subject>Mining industry</subject><subject>Modelling</subject><subject>Molybdenum</subject><subject>Neural networks</subject><subject>Ores</subject><subject>Particle size</subject><subject>Performance evaluation</subject><subject>Production costs</subject><subject>Production management</subject><subject>Research and Analysis Methods</subject><subject>Rock masses</subject><subject>Rocks</subject><subject>Semantics</subject><subject>Social Sciences</subject><subject>Statistical analysis</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl-L1DAUxYsouK5-A8GCIOtDx6Rpk_RJhsHVgYWFXVcfQ5rcdjK0ydik4n5705kqW9kHyUP-_e5J7uEkyWuMVpgw_GHvxsHKbnVwFlYorzDG5ZPkDFckz2iOyNMH6-fJC-_3CJWEU3qWfL8No75PnU09tD3YIIOJG9ekdSd9MLZNm0Eeb1LTyxZ83Ls-dQew2cGEtDcW0lp60JPIXbZZ31xaCC-TZ43sPLya5_Pk7vLT182X7Or683azvsoUxVXIdFVxYKygrESKkaoCSrRkNK8LzRmrKEK8prkqUc64Aq2Lkiota0JANQ0qyHny5qR76JwXsxFe5JwWnDPKeCS2J0I7uReHIXYx3AsnjTgeuKEVcghGdSAUBkV4DoXOUdHoWgLHZckkUogwWcio9XF-bax70Cq6MshuIbq8sWYnWvdTYFQiHJuMChezwuB-jOCD6I1X0HXSghuPHy8ZzzGZ0Lf_oI-3N1OtjB0Y27j4sJpExZrRkuCC8cmm1SNUHBp6o2JsGhPPFwXvFwWRCfArtHL0Xmxvb_6fvf62ZN89YHcgu7Dzrhun1PklWJxANTjvB2j-uoyRmFL_xw0xpV7MqSe_AaiK868</recordid><startdate>20230914</startdate><enddate>20230914</enddate><creator>Jin, Changyu</creator><creator>Liang, Junyu</creator><creator>Fan, Chunhui</creator><creator>Chen, Lijun</creator><creator>Wang, Qiang</creator><creator>Lu, Yu</creator><creator>Wang, Kai</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7656-4644</orcidid></search><sort><creationdate>20230914</creationdate><title>Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet</title><author>Jin, Changyu ; Liang, Junyu ; Fan, Chunhui ; Chen, Lijun ; Wang, Qiang ; Lu, Yu ; Wang, Kai</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c619t-d998e7746750c7399e63da762b4d87796008b62c50278cedd456cdab33ecff043</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Analysis</topic><topic>Biology and Life Sciences</topic><topic>Blasting</topic><topic>Computer and Information Sciences</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Engineering and Technology</topic><topic>Image processing</topic><topic>Image segmentation</topic><topic>Iron compounds</topic><topic>Management</topic><topic>Medical imaging equipment</topic><topic>Microscopy</topic><topic>Mineral industry</topic><topic>Mines</topic><topic>Mining</topic><topic>Mining engineering</topic><topic>Mining industry</topic><topic>Modelling</topic><topic>Molybdenum</topic><topic>Neural networks</topic><topic>Ores</topic><topic>Particle size</topic><topic>Performance evaluation</topic><topic>Production costs</topic><topic>Production management</topic><topic>Research and Analysis Methods</topic><topic>Rock masses</topic><topic>Rocks</topic><topic>Semantics</topic><topic>Social Sciences</topic><topic>Statistical analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jin, Changyu</creatorcontrib><creatorcontrib>Liang, Junyu</creatorcontrib><creatorcontrib>Fan, Chunhui</creatorcontrib><creatorcontrib>Chen, Lijun</creatorcontrib><creatorcontrib>Wang, Qiang</creatorcontrib><creatorcontrib>Lu, Yu</creatorcontrib><creatorcontrib>Wang, Kai</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing &amp; Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jin, Changyu</au><au>Liang, Junyu</au><au>Fan, Chunhui</au><au>Chen, Lijun</au><au>Wang, Qiang</au><au>Lu, Yu</au><au>Wang, Kai</au><au>Peña, Mª Ángeles</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet</atitle><jtitle>PloS one</jtitle><date>2023-09-14</date><risdate>2023</risdate><volume>18</volume><issue>9</issue><spage>e0291115</spage><pages>e0291115-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Bench blasting is the primary means of production in open-pit metal mines. The size of the resulting rock mass after blasting has a significant impact on production cost. Currently, the ore fragment size is obtained mainly through manual measurement or estimation with the naked eye, which is inefficient and inaccurate. This study proposes the U-CARFnet and U-Net models for segmenting blasting fragment images from open-pit mines based on an attention mechanism, residual learning module, and focal loss function. It compares this technique with traditional image segmentation ones and a variety of deep learning models to verify the efficacy of the proposed model. Experimental results show that the accuracy of the U-CARFnet model proposed in this paper reaches 97.11% in the performance evaluation, which shows better performance than the traditional image segmentation method. In this study, the U-CARFnet model is used in the application, and a superior performance is obtained, with an average segmentation error of 5.46%. The proposed approach provides an effective technique for statistically analyzing images of mine rock.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><doi>10.1371/journal.pone.0291115</doi><tpages>e0291115</tpages><orcidid>https://orcid.org/0000-0001-7656-4644</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1932-6203
ispartof PloS one, 2023-09, Vol.18 (9), p.e0291115
issn 1932-6203
1932-6203
language eng
recordid cdi_plos_journals_2864887678
source DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry
subjects Algorithms
Analysis
Biology and Life Sciences
Blasting
Computer and Information Sciences
Datasets
Deep learning
Engineering and Technology
Image processing
Image segmentation
Iron compounds
Management
Medical imaging equipment
Microscopy
Mineral industry
Mines
Mining
Mining engineering
Mining industry
Modelling
Molybdenum
Neural networks
Ores
Particle size
Performance evaluation
Production costs
Production management
Research and Analysis Methods
Rock masses
Rocks
Semantics
Social Sciences
Statistical analysis
title Study on segmentation of blasting fragment images from open-pit mine based on U-CARFnet
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T00%3A46%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Study%20on%20segmentation%20of%20blasting%20fragment%20images%20from%20open-pit%20mine%20based%20on%20U-CARFnet&rft.jtitle=PloS%20one&rft.au=Jin,%20Changyu&rft.date=2023-09-14&rft.volume=18&rft.issue=9&rft.spage=e0291115&rft.pages=e0291115-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0291115&rft_dat=%3Cgale_plos_%3EA765314784%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2864887678&rft_id=info:pmid/&rft_galeid=A765314784&rft_doaj_id=oai_doaj_org_article_c1ec382e4d204fdbae81557a0c037a4a&rfr_iscdi=true