GIS multi-source partial discharge mode identification method and device, equipment and storage medium
The invention provides a GIS multi-source partial discharge mode recognition method and device, equipment and a storage medium, and belongs to the field of computer vision, and the method comprises the steps: building a backbone network of a YOLOv5 model based on a ShuffleNetV2 network, recognizing...
Gespeichert in:
Hauptverfasser: | , , , , , , , , , |
---|---|
Format: | Patent |
Sprache: | chi ; eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
creator | HUANG YONGLIANG LI JUNBIN YU XINQIU YANG KAI CHEN QI YI XIAOBO LI WENLONG JIANG WEI WU YOUYU WEN ZHAOLONG |
description | The invention provides a GIS multi-source partial discharge mode recognition method and device, equipment and a storage medium, and belongs to the field of computer vision, and the method comprises the steps: building a backbone network of a YOLOv5 model based on a ShuffleNetV2 network, recognizing a plurality of target modules from a large number of ShuffleNetV2 modules in the network based on the number and arrangement mode of shuffling units, and carrying out the recognition of the ShuffleNetV2 modules in the network, the redundant first convolution layer with the convolution kernel of 1 * 1 in the target module is removed through branch replacement, and the calculation amount of convolution calculation is reduced; according to the method, the to-be-detected PRPD atlas is input into the YOLOv5 model, the number of channels of an extracted module mixed feature map is small due to the fact that a first convolutional layer is reduced, the attention degree of high-dimensional semantic information is increased |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN118366008A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN118366008A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN118366008A3</originalsourceid><addsrcrecordid>eNqNi7sKwkAQANNYiPoPa28gIRBsJfhqbLQPy-3GLOQe3u35_T7wA6wGhpl5MRzPV7B5UimTz9EwBIwqOAFJMiPGO4P1xCDETmUQgyregWUdPQE6AuKnGN4AP7IE-66-NqmP-JmZJNtlMRtwSrz6cVGsD_tbdyo5-J5TQMOOte8udb1t2raqtrvmn-YFVUQ_cg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>GIS multi-source partial discharge mode identification method and device, equipment and storage medium</title><source>esp@cenet</source><creator>HUANG YONGLIANG ; LI JUNBIN ; YU XINQIU ; YANG KAI ; CHEN QI ; YI XIAOBO ; LI WENLONG ; JIANG WEI ; WU YOUYU ; WEN ZHAOLONG</creator><creatorcontrib>HUANG YONGLIANG ; LI JUNBIN ; YU XINQIU ; YANG KAI ; CHEN QI ; YI XIAOBO ; LI WENLONG ; JIANG WEI ; WU YOUYU ; WEN ZHAOLONG</creatorcontrib><description>The invention provides a GIS multi-source partial discharge mode recognition method and device, equipment and a storage medium, and belongs to the field of computer vision, and the method comprises the steps: building a backbone network of a YOLOv5 model based on a ShuffleNetV2 network, recognizing a plurality of target modules from a large number of ShuffleNetV2 modules in the network based on the number and arrangement mode of shuffling units, and carrying out the recognition of the ShuffleNetV2 modules in the network, the redundant first convolution layer with the convolution kernel of 1 * 1 in the target module is removed through branch replacement, and the calculation amount of convolution calculation is reduced; according to the method, the to-be-detected PRPD atlas is input into the YOLOv5 model, the number of channels of an extracted module mixed feature map is small due to the fact that a first convolutional layer is reduced, the attention degree of high-dimensional semantic information is increased</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240719&DB=EPODOC&CC=CN&NR=118366008A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240719&DB=EPODOC&CC=CN&NR=118366008A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HUANG YONGLIANG</creatorcontrib><creatorcontrib>LI JUNBIN</creatorcontrib><creatorcontrib>YU XINQIU</creatorcontrib><creatorcontrib>YANG KAI</creatorcontrib><creatorcontrib>CHEN QI</creatorcontrib><creatorcontrib>YI XIAOBO</creatorcontrib><creatorcontrib>LI WENLONG</creatorcontrib><creatorcontrib>JIANG WEI</creatorcontrib><creatorcontrib>WU YOUYU</creatorcontrib><creatorcontrib>WEN ZHAOLONG</creatorcontrib><title>GIS multi-source partial discharge mode identification method and device, equipment and storage medium</title><description>The invention provides a GIS multi-source partial discharge mode recognition method and device, equipment and a storage medium, and belongs to the field of computer vision, and the method comprises the steps: building a backbone network of a YOLOv5 model based on a ShuffleNetV2 network, recognizing a plurality of target modules from a large number of ShuffleNetV2 modules in the network based on the number and arrangement mode of shuffling units, and carrying out the recognition of the ShuffleNetV2 modules in the network, the redundant first convolution layer with the convolution kernel of 1 * 1 in the target module is removed through branch replacement, and the calculation amount of convolution calculation is reduced; according to the method, the to-be-detected PRPD atlas is input into the YOLOv5 model, the number of channels of an extracted module mixed feature map is small due to the fact that a first convolutional layer is reduced, the attention degree of high-dimensional semantic information is increased</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNi7sKwkAQANNYiPoPa28gIRBsJfhqbLQPy-3GLOQe3u35_T7wA6wGhpl5MRzPV7B5UimTz9EwBIwqOAFJMiPGO4P1xCDETmUQgyregWUdPQE6AuKnGN4AP7IE-66-NqmP-JmZJNtlMRtwSrz6cVGsD_tbdyo5-J5TQMOOte8udb1t2raqtrvmn-YFVUQ_cg</recordid><startdate>20240719</startdate><enddate>20240719</enddate><creator>HUANG YONGLIANG</creator><creator>LI JUNBIN</creator><creator>YU XINQIU</creator><creator>YANG KAI</creator><creator>CHEN QI</creator><creator>YI XIAOBO</creator><creator>LI WENLONG</creator><creator>JIANG WEI</creator><creator>WU YOUYU</creator><creator>WEN ZHAOLONG</creator><scope>EVB</scope></search><sort><creationdate>20240719</creationdate><title>GIS multi-source partial discharge mode identification method and device, equipment and storage medium</title><author>HUANG YONGLIANG ; LI JUNBIN ; YU XINQIU ; YANG KAI ; CHEN QI ; YI XIAOBO ; LI WENLONG ; JIANG WEI ; WU YOUYU ; WEN ZHAOLONG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118366008A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>HUANG YONGLIANG</creatorcontrib><creatorcontrib>LI JUNBIN</creatorcontrib><creatorcontrib>YU XINQIU</creatorcontrib><creatorcontrib>YANG KAI</creatorcontrib><creatorcontrib>CHEN QI</creatorcontrib><creatorcontrib>YI XIAOBO</creatorcontrib><creatorcontrib>LI WENLONG</creatorcontrib><creatorcontrib>JIANG WEI</creatorcontrib><creatorcontrib>WU YOUYU</creatorcontrib><creatorcontrib>WEN ZHAOLONG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HUANG YONGLIANG</au><au>LI JUNBIN</au><au>YU XINQIU</au><au>YANG KAI</au><au>CHEN QI</au><au>YI XIAOBO</au><au>LI WENLONG</au><au>JIANG WEI</au><au>WU YOUYU</au><au>WEN ZHAOLONG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>GIS multi-source partial discharge mode identification method and device, equipment and storage medium</title><date>2024-07-19</date><risdate>2024</risdate><abstract>The invention provides a GIS multi-source partial discharge mode recognition method and device, equipment and a storage medium, and belongs to the field of computer vision, and the method comprises the steps: building a backbone network of a YOLOv5 model based on a ShuffleNetV2 network, recognizing a plurality of target modules from a large number of ShuffleNetV2 modules in the network based on the number and arrangement mode of shuffling units, and carrying out the recognition of the ShuffleNetV2 modules in the network, the redundant first convolution layer with the convolution kernel of 1 * 1 in the target module is removed through branch replacement, and the calculation amount of convolution calculation is reduced; according to the method, the to-be-detected PRPD atlas is input into the YOLOv5 model, the number of channels of an extracted module mixed feature map is small due to the fact that a first convolutional layer is reduced, the attention degree of high-dimensional semantic information is increased</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | chi ; eng |
recordid | cdi_epo_espacenet_CN118366008A |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
title | GIS multi-source partial discharge mode identification method and device, equipment and storage medium |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T21%3A00%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=HUANG%20YONGLIANG&rft.date=2024-07-19&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN118366008A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |