Hazard source detection of longitudinal tearing of conveyor belt based on deep learning
Belt tearing is the main safety accident of belt conveyor. The main cause of tearing is the doped bolt and steel in the conveying belt. In this paper, the bolt and steel are identified as the Hazard source of tear. In this paper, bolt and steel are defined as the risk sources of tearing. Effective d...
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| Veröffentlicht in: | PloS one 2023-04, Vol.18 (4), p.e0283878-e0283878 |
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| creator | Wang, Yimin Miao, Changyun Miao, Di Yang, Dengjie Zheng, Yao |
| description | Belt tearing is the main safety accident of belt conveyor. The main cause of tearing is the doped bolt and steel in the conveying belt. In this paper, the bolt and steel are identified as the Hazard source of tear. In this paper, bolt and steel are defined as the risk sources of tearing. Effective detection of the source of danger can effectively prevent the occurrence of conveyor belt tearing accidents. Here we use deep learning to detect the hazard source image. We improved on the SSD(Single Shot MultiBox Detector) model. Replace the original backbone network with an improved Shufflenet_V2, and replace the original position loss function with the CIoU loss function. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 94% accuracy. In addition, when deployed without GPU acceleration, the detection speed can reach 20fps. It can meet the requirements of real-time detection. The experimental results show that the proposed model can realize the online detection of hazard sources, so as to prevent longitudinal tearing of conveyor belt. |
| doi_str_mv | 10.1371/journal.pone.0283878 |
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The main cause of tearing is the doped bolt and steel in the conveying belt. In this paper, the bolt and steel are identified as the Hazard source of tear. In this paper, bolt and steel are defined as the risk sources of tearing. Effective detection of the source of danger can effectively prevent the occurrence of conveyor belt tearing accidents. Here we use deep learning to detect the hazard source image. We improved on the SSD(Single Shot MultiBox Detector) model. Replace the original backbone network with an improved Shufflenet_V2, and replace the original position loss function with the CIoU loss function. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 94% accuracy. In addition, when deployed without GPU acceleration, the detection speed can reach 20fps. It can meet the requirements of real-time detection. The experimental results show that the proposed model can realize the online detection of hazard sources, so as to prevent longitudinal tearing of conveyor belt.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0283878</identifier><identifier>PMID: 37023047</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acceleration ; Accidents ; Accuracy ; Algorithms ; Belt conveyors ; Biology and Life Sciences ; Cameras ; Coal ; Computer and Information Sciences ; Computer networks ; Conveying machinery ; Deep Learning ; Detectors ; Diabetic retinopathy ; Engineering and Technology ; Humans ; Lacrimal Apparatus Diseases ; Management ; Medicine and Health Sciences ; Metal detectors ; Methods ; Mineral industry ; Mining industry ; Neural networks ; Occupational health and safety ; Physical Sciences ; Research and Analysis Methods ; Safety and security measures ; Seat belts ; Steel ; Tearing ; Telecommunication systems ; Vision systems</subject><ispartof>PloS one, 2023-04, Vol.18 (4), p.e0283878-e0283878</ispartof><rights>Copyright: © 2023 Wang 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>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Wang 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>2023 Wang et al 2023 Wang et al</rights><rights>2023 Wang 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><citedby>FETCH-LOGICAL-c693t-f12c1a8ffd9e0787f831e9796eda9ca9370d566c1fe085df492db6f54b1371dd3</citedby><cites>FETCH-LOGICAL-c693t-f12c1a8ffd9e0787f831e9796eda9ca9370d566c1fe085df492db6f54b1371dd3</cites><orcidid>0000-0003-4455-9212</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/PMC10079013/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10079013/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37023047$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Ijaz, Muhammad Fazal</contributor><creatorcontrib>Wang, Yimin</creatorcontrib><creatorcontrib>Miao, Changyun</creatorcontrib><creatorcontrib>Miao, Di</creatorcontrib><creatorcontrib>Yang, Dengjie</creatorcontrib><creatorcontrib>Zheng, Yao</creatorcontrib><title>Hazard source detection of longitudinal tearing of conveyor belt based on deep learning</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Belt tearing is the main safety accident of belt conveyor. The main cause of tearing is the doped bolt and steel in the conveying belt. In this paper, the bolt and steel are identified as the Hazard source of tear. In this paper, bolt and steel are defined as the risk sources of tearing. Effective detection of the source of danger can effectively prevent the occurrence of conveyor belt tearing accidents. Here we use deep learning to detect the hazard source image. We improved on the SSD(Single Shot MultiBox Detector) model. Replace the original backbone network with an improved Shufflenet_V2, and replace the original position loss function with the CIoU loss function. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 94% accuracy. In addition, when deployed without GPU acceleration, the detection speed can reach 20fps. It can meet the requirements of real-time detection. 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One</addtitle><date>2023-04-06</date><risdate>2023</risdate><volume>18</volume><issue>4</issue><spage>e0283878</spage><epage>e0283878</epage><pages>e0283878-e0283878</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Belt tearing is the main safety accident of belt conveyor. The main cause of tearing is the doped bolt and steel in the conveying belt. In this paper, the bolt and steel are identified as the Hazard source of tear. In this paper, bolt and steel are defined as the risk sources of tearing. Effective detection of the source of danger can effectively prevent the occurrence of conveyor belt tearing accidents. Here we use deep learning to detect the hazard source image. We improved on the SSD(Single Shot MultiBox Detector) model. Replace the original backbone network with an improved Shufflenet_V2, and replace the original position loss function with the CIoU loss function. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 94% accuracy. In addition, when deployed without GPU acceleration, the detection speed can reach 20fps. It can meet the requirements of real-time detection. The experimental results show that the proposed model can realize the online detection of hazard sources, so as to prevent longitudinal tearing of conveyor belt.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>37023047</pmid><doi>10.1371/journal.pone.0283878</doi><tpages>e0283878</tpages><orcidid>https://orcid.org/0000-0003-4455-9212</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Acceleration Accidents Accuracy Algorithms Belt conveyors Biology and Life Sciences Cameras Coal Computer and Information Sciences Computer networks Conveying machinery Deep Learning Detectors Diabetic retinopathy Engineering and Technology Humans Lacrimal Apparatus Diseases Management Medicine and Health Sciences Metal detectors Methods Mineral industry Mining industry Neural networks Occupational health and safety Physical Sciences Research and Analysis Methods Safety and security measures Seat belts Steel Tearing Telecommunication systems Vision systems |
| title | Hazard source detection of longitudinal tearing of conveyor belt based on deep learning |
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