PVC pipe size detection method and device based on high-resolution semantic segmentation
The invention discloses a PVC pipe size detection method and device based on high-resolution semantic segmentation, and the method comprises the steps: S01, obtaining an original image, and carrying out the transformation of the original image, and obtaining a to-be-segmented image; s02, the image t...
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 | XIANG CHAOQIAN LI RUOLONG AI YANDI XU XUESONG |
| description | The invention discloses a PVC pipe size detection method and device based on high-resolution semantic segmentation, and the method comprises the steps: S01, obtaining an original image, and carrying out the transformation of the original image, and obtaining a to-be-segmented image; s02, the image to be segmented is input into a high-resolution semantic segmentation model for semantic segmentation, the high-resolution semantic segmentation model is based on an ISDNet model, in an RAF module, attention layer parameters are generated for a shallow feature map and a deep feature map through two parallel branches respectively, attention layer parameters are fused through MLP layer fusion, and the attention layer parameters of shallow channels and deep channels are fused; fusing with a space attention module to obtain a final space attention parameter; step S03, extracting a pipeline edge region from a high-resolution semantic segmentation result; and step S04, extracting size information of the PVC pipeline from |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN118397072A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN118397072A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN118397072A3</originalsourceid><addsrcrecordid>eNrjZIgICHNWKMgsSFUozqxKVUhJLUlNLsnMz1PITS3JyE9RSMxLAQqWZSanKiQlFqemKAClMjLTM3SLUovzc0rBSotTcxPzSjKTgYz03NS8kkSQKA8Da1piTnEqL5TmZlB0cw1x9tBNLciPTy0uSExOzUstiXf2MzS0MLY0NzA3cjQmRg0AbwM6Pw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>PVC pipe size detection method and device based on high-resolution semantic segmentation</title><source>esp@cenet</source><creator>XIANG CHAOQIAN ; LI RUOLONG ; AI YANDI ; XU XUESONG</creator><creatorcontrib>XIANG CHAOQIAN ; LI RUOLONG ; AI YANDI ; XU XUESONG</creatorcontrib><description>The invention discloses a PVC pipe size detection method and device based on high-resolution semantic segmentation, and the method comprises the steps: S01, obtaining an original image, and carrying out the transformation of the original image, and obtaining a to-be-segmented image; s02, the image to be segmented is input into a high-resolution semantic segmentation model for semantic segmentation, the high-resolution semantic segmentation model is based on an ISDNet model, in an RAF module, attention layer parameters are generated for a shallow feature map and a deep feature map through two parallel branches respectively, attention layer parameters are fused through MLP layer fusion, and the attention layer parameters of shallow channels and deep channels are fused; fusing with a space attention module to obtain a final space attention parameter; step S03, extracting a pipeline edge region from a high-resolution semantic segmentation result; and step S04, extracting size information of the PVC pipeline from</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; 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=20240726&DB=EPODOC&CC=CN&NR=118397072A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240726&DB=EPODOC&CC=CN&NR=118397072A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>XIANG CHAOQIAN</creatorcontrib><creatorcontrib>LI RUOLONG</creatorcontrib><creatorcontrib>AI YANDI</creatorcontrib><creatorcontrib>XU XUESONG</creatorcontrib><title>PVC pipe size detection method and device based on high-resolution semantic segmentation</title><description>The invention discloses a PVC pipe size detection method and device based on high-resolution semantic segmentation, and the method comprises the steps: S01, obtaining an original image, and carrying out the transformation of the original image, and obtaining a to-be-segmented image; s02, the image to be segmented is input into a high-resolution semantic segmentation model for semantic segmentation, the high-resolution semantic segmentation model is based on an ISDNet model, in an RAF module, attention layer parameters are generated for a shallow feature map and a deep feature map through two parallel branches respectively, attention layer parameters are fused through MLP layer fusion, and the attention layer parameters of shallow channels and deep channels are fused; fusing with a space attention module to obtain a final space attention parameter; step S03, extracting a pipeline edge region from a high-resolution semantic segmentation result; and step S04, extracting size information of the PVC pipeline from</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZIgICHNWKMgsSFUozqxKVUhJLUlNLsnMz1PITS3JyE9RSMxLAQqWZSanKiQlFqemKAClMjLTM3SLUovzc0rBSotTcxPzSjKTgYz03NS8kkSQKA8Da1piTnEqL5TmZlB0cw1x9tBNLciPTy0uSExOzUstiXf2MzS0MLY0NzA3cjQmRg0AbwM6Pw</recordid><startdate>20240726</startdate><enddate>20240726</enddate><creator>XIANG CHAOQIAN</creator><creator>LI RUOLONG</creator><creator>AI YANDI</creator><creator>XU XUESONG</creator><scope>EVB</scope></search><sort><creationdate>20240726</creationdate><title>PVC pipe size detection method and device based on high-resolution semantic segmentation</title><author>XIANG CHAOQIAN ; LI RUOLONG ; AI YANDI ; XU XUESONG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118397072A3</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>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>XIANG CHAOQIAN</creatorcontrib><creatorcontrib>LI RUOLONG</creatorcontrib><creatorcontrib>AI YANDI</creatorcontrib><creatorcontrib>XU XUESONG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>XIANG CHAOQIAN</au><au>LI RUOLONG</au><au>AI YANDI</au><au>XU XUESONG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>PVC pipe size detection method and device based on high-resolution semantic segmentation</title><date>2024-07-26</date><risdate>2024</risdate><abstract>The invention discloses a PVC pipe size detection method and device based on high-resolution semantic segmentation, and the method comprises the steps: S01, obtaining an original image, and carrying out the transformation of the original image, and obtaining a to-be-segmented image; s02, the image to be segmented is input into a high-resolution semantic segmentation model for semantic segmentation, the high-resolution semantic segmentation model is based on an ISDNet model, in an RAF module, attention layer parameters are generated for a shallow feature map and a deep feature map through two parallel branches respectively, attention layer parameters are fused through MLP layer fusion, and the attention layer parameters of shallow channels and deep channels are fused; fusing with a space attention module to obtain a final space attention parameter; step S03, extracting a pipeline edge region from a high-resolution semantic segmentation result; and step S04, extracting size information of the PVC pipeline from</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN118397072A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
| title | PVC pipe size detection method and device based on high-resolution semantic segmentation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-26T01%3A00%3A55IST&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=XIANG%20CHAOQIAN&rft.date=2024-07-26&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN118397072A%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 |