Power line inspection method based on lightweight target recognition neural network model
The invention provides a power line inspection method based on a lightweight target recognition neural network model, and the method comprises the steps: 1, carrying out the inspection of a power grid transmission line through an unmanned plane, and obtaining a power grid line image through the unma...
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 | SUN WENWEN CHEN JIANBO WANG QIAN TANG RUI ZHANG SAIFEI ZOU DEFAN ZHANG NAN YAO PING HE YUCHEN LIAO LIN GONG QIBO YANG CHUNPING LUO HUI |
| description | The invention provides a power line inspection method based on a lightweight target recognition neural network model, and the method comprises the steps: 1, carrying out the inspection of a power grid transmission line through an unmanned plane, and obtaining a power grid line image through the unmanned plane; 2, constructing a lightweight target recognition neural network model, and inputting the image into the lightweight target recognition neural network model for recognition; and step 3, obtaining a recognition result of the power grid line image according to the output of the lightweight target recognition neural network model, wherein the recognition result comprises the category and the fault position of the power grid line. According to the method, embedded hardware computing power is considered, neural network parameters with high density are randomly discarded by combining a Bernoulli equation of the hardware computing power based on a deep separation convolutional network, and meanwhile, the recogn |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116052024A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116052024A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116052024A3</originalsourceid><addsrcrecordid>eNqNyr0KwjAYheEsDqLew-cFCG392aUoTuLg4lRiekyDab6QRHL7RvECXM7LgWcqbhfOCGSNAxkXPVQy7GhEGrinu4zoqXxr9JAyPktJBo1EAYq1M1_u8ArSlqTM4Ukj97BzMXlIG7H4dSaWx8O1Pa3guUP0UqH4rj3X9a7aNlWz2a__MW8xzjtC</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Power line inspection method based on lightweight target recognition neural network model</title><source>esp@cenet</source><creator>SUN WENWEN ; CHEN JIANBO ; WANG QIAN ; TANG RUI ; ZHANG SAIFEI ; ZOU DEFAN ; ZHANG NAN ; YAO PING ; HE YUCHEN ; LIAO LIN ; GONG QIBO ; YANG CHUNPING ; LUO HUI</creator><creatorcontrib>SUN WENWEN ; CHEN JIANBO ; WANG QIAN ; TANG RUI ; ZHANG SAIFEI ; ZOU DEFAN ; ZHANG NAN ; YAO PING ; HE YUCHEN ; LIAO LIN ; GONG QIBO ; YANG CHUNPING ; LUO HUI</creatorcontrib><description>The invention provides a power line inspection method based on a lightweight target recognition neural network model, and the method comprises the steps: 1, carrying out the inspection of a power grid transmission line through an unmanned plane, and obtaining a power grid line image through the unmanned plane; 2, constructing a lightweight target recognition neural network model, and inputting the image into the lightweight target recognition neural network model for recognition; and step 3, obtaining a recognition result of the power grid line image according to the output of the lightweight target recognition neural network model, wherein the recognition result comprises the category and the fault position of the power grid line. According to the method, embedded hardware computing power is considered, neural network parameters with high density are randomly discarded by combining a Bernoulli equation of the hardware computing power based on a deep separation convolutional network, and meanwhile, the recogn</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS</subject><creationdate>2023</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=20230502&DB=EPODOC&CC=CN&NR=116052024A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230502&DB=EPODOC&CC=CN&NR=116052024A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SUN WENWEN</creatorcontrib><creatorcontrib>CHEN JIANBO</creatorcontrib><creatorcontrib>WANG QIAN</creatorcontrib><creatorcontrib>TANG RUI</creatorcontrib><creatorcontrib>ZHANG SAIFEI</creatorcontrib><creatorcontrib>ZOU DEFAN</creatorcontrib><creatorcontrib>ZHANG NAN</creatorcontrib><creatorcontrib>YAO PING</creatorcontrib><creatorcontrib>HE YUCHEN</creatorcontrib><creatorcontrib>LIAO LIN</creatorcontrib><creatorcontrib>GONG QIBO</creatorcontrib><creatorcontrib>YANG CHUNPING</creatorcontrib><creatorcontrib>LUO HUI</creatorcontrib><title>Power line inspection method based on lightweight target recognition neural network model</title><description>The invention provides a power line inspection method based on a lightweight target recognition neural network model, and the method comprises the steps: 1, carrying out the inspection of a power grid transmission line through an unmanned plane, and obtaining a power grid line image through the unmanned plane; 2, constructing a lightweight target recognition neural network model, and inputting the image into the lightweight target recognition neural network model for recognition; and step 3, obtaining a recognition result of the power grid line image according to the output of the lightweight target recognition neural network model, wherein the recognition result comprises the category and the fault position of the power grid line. According to the method, embedded hardware computing power is considered, neural network parameters with high density are randomly discarded by combining a Bernoulli equation of the hardware computing power based on a deep separation convolutional network, and meanwhile, the recogn</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>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNyr0KwjAYheEsDqLew-cFCG392aUoTuLg4lRiekyDab6QRHL7RvECXM7LgWcqbhfOCGSNAxkXPVQy7GhEGrinu4zoqXxr9JAyPktJBo1EAYq1M1_u8ArSlqTM4Ukj97BzMXlIG7H4dSaWx8O1Pa3guUP0UqH4rj3X9a7aNlWz2a__MW8xzjtC</recordid><startdate>20230502</startdate><enddate>20230502</enddate><creator>SUN WENWEN</creator><creator>CHEN JIANBO</creator><creator>WANG QIAN</creator><creator>TANG RUI</creator><creator>ZHANG SAIFEI</creator><creator>ZOU DEFAN</creator><creator>ZHANG NAN</creator><creator>YAO PING</creator><creator>HE YUCHEN</creator><creator>LIAO LIN</creator><creator>GONG QIBO</creator><creator>YANG CHUNPING</creator><creator>LUO HUI</creator><scope>EVB</scope></search><sort><creationdate>20230502</creationdate><title>Power line inspection method based on lightweight target recognition neural network model</title><author>SUN WENWEN ; CHEN JIANBO ; WANG QIAN ; TANG RUI ; ZHANG SAIFEI ; ZOU DEFAN ; ZHANG NAN ; YAO PING ; HE YUCHEN ; LIAO LIN ; GONG QIBO ; YANG CHUNPING ; LUO HUI</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116052024A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</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>SUN WENWEN</creatorcontrib><creatorcontrib>CHEN JIANBO</creatorcontrib><creatorcontrib>WANG QIAN</creatorcontrib><creatorcontrib>TANG RUI</creatorcontrib><creatorcontrib>ZHANG SAIFEI</creatorcontrib><creatorcontrib>ZOU DEFAN</creatorcontrib><creatorcontrib>ZHANG NAN</creatorcontrib><creatorcontrib>YAO PING</creatorcontrib><creatorcontrib>HE YUCHEN</creatorcontrib><creatorcontrib>LIAO LIN</creatorcontrib><creatorcontrib>GONG QIBO</creatorcontrib><creatorcontrib>YANG CHUNPING</creatorcontrib><creatorcontrib>LUO HUI</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>SUN WENWEN</au><au>CHEN JIANBO</au><au>WANG QIAN</au><au>TANG RUI</au><au>ZHANG SAIFEI</au><au>ZOU DEFAN</au><au>ZHANG NAN</au><au>YAO PING</au><au>HE YUCHEN</au><au>LIAO LIN</au><au>GONG QIBO</au><au>YANG CHUNPING</au><au>LUO HUI</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Power line inspection method based on lightweight target recognition neural network model</title><date>2023-05-02</date><risdate>2023</risdate><abstract>The invention provides a power line inspection method based on a lightweight target recognition neural network model, and the method comprises the steps: 1, carrying out the inspection of a power grid transmission line through an unmanned plane, and obtaining a power grid line image through the unmanned plane; 2, constructing a lightweight target recognition neural network model, and inputting the image into the lightweight target recognition neural network model for recognition; and step 3, obtaining a recognition result of the power grid line image according to the output of the lightweight target recognition neural network model, wherein the recognition result comprises the category and the fault position of the power grid line. According to the method, embedded hardware computing power is considered, neural network parameters with high density are randomly discarded by combining a Bernoulli equation of the hardware computing power based on a deep separation convolutional network, and meanwhile, the recogn</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN116052024A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | Power line inspection method based on lightweight target recognition neural network model |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-11T09%3A21%3A37IST&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=SUN%20WENWEN&rft.date=2023-05-02&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116052024A%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 |