Wheat scab spore identification method based on YOLOv4-tiny lightweight model
The invention relates to a wheat gibberellic disease spore identification method based on a YOLOv4-tiny lightweight model. Compared with the prior art, the wheat gibberellic disease spore identification method solves the defects that gibberellic disease spore detection is low in accuracy, low in det...
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 | ZHANG HANSU CHANG HAOYU CHEN XU SHIM CHAN-JOON LI SHICHANG ZHANG SHENGYU ZHAN ZICHAO HOU ZHIMENG LI HAIDONG YU SHUAN SHANG JUNCHENG |
| description | The invention relates to a wheat gibberellic disease spore identification method based on a YOLOv4-tiny lightweight model. Compared with the prior art, the wheat gibberellic disease spore identification method solves the defects that gibberellic disease spore detection is low in accuracy, low in detection speed and large in calculation amount. The method comprises the following steps: establishing a spore image data set; constructing a lightweight spore recognition model; training a lightweight spore recognition model; acquiring a spore image to be identified; and obtaining a wheat scab spore identification result. According to the method, the characteristics of higher detection speed and better real-time performance of YOLOv4-tin are utilized, and rapid detection is realized while the accuracy is ensured; according to the method, the CSPDarknet53 module of the YOLOv4 trunk feature extraction network is replaced by the CSPDarknet53tiny module, so that rapid and accurate detection and identification of the whe |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN117197807A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN117197807A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN117197807A3</originalsourceid><addsrcrecordid>eNrjZPANz0hNLFEoTk5MUiguyC9KVchMSc0ryUzLTE4syczPU8hNLcnIT1FISixOTVEA8iP9ffzLTHRLMvMqFXIy0zNKylNBpEJufkpqDg8Da1piTnEqL5TmZlB0cw1x9tBNLciPTy0uSExOzUstiXf2MzQ0N7Q0tzAwdzQmRg0Ap4w11w</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Wheat scab spore identification method based on YOLOv4-tiny lightweight model</title><source>esp@cenet</source><creator>ZHANG HANSU ; CHANG HAOYU ; CHEN XU ; SHIM CHAN-JOON ; LI SHICHANG ; ZHANG SHENGYU ; ZHAN ZICHAO ; HOU ZHIMENG ; LI HAIDONG ; YU SHUAN ; SHANG JUNCHENG</creator><creatorcontrib>ZHANG HANSU ; CHANG HAOYU ; CHEN XU ; SHIM CHAN-JOON ; LI SHICHANG ; ZHANG SHENGYU ; ZHAN ZICHAO ; HOU ZHIMENG ; LI HAIDONG ; YU SHUAN ; SHANG JUNCHENG</creatorcontrib><description>The invention relates to a wheat gibberellic disease spore identification method based on a YOLOv4-tiny lightweight model. Compared with the prior art, the wheat gibberellic disease spore identification method solves the defects that gibberellic disease spore detection is low in accuracy, low in detection speed and large in calculation amount. The method comprises the following steps: establishing a spore image data set; constructing a lightweight spore recognition model; training a lightweight spore recognition model; acquiring a spore image to be identified; and obtaining a wheat scab spore identification result. According to the method, the characteristics of higher detection speed and better real-time performance of YOLOv4-tin are utilized, and rapid detection is realized while the accuracy is ensured; according to the method, the CSPDarknet53 module of the YOLOv4 trunk feature extraction network is replaced by the CSPDarknet53tiny module, so that rapid and accurate detection and identification of the whe</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=20231208&DB=EPODOC&CC=CN&NR=117197807A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20231208&DB=EPODOC&CC=CN&NR=117197807A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHANG HANSU</creatorcontrib><creatorcontrib>CHANG HAOYU</creatorcontrib><creatorcontrib>CHEN XU</creatorcontrib><creatorcontrib>SHIM CHAN-JOON</creatorcontrib><creatorcontrib>LI SHICHANG</creatorcontrib><creatorcontrib>ZHANG SHENGYU</creatorcontrib><creatorcontrib>ZHAN ZICHAO</creatorcontrib><creatorcontrib>HOU ZHIMENG</creatorcontrib><creatorcontrib>LI HAIDONG</creatorcontrib><creatorcontrib>YU SHUAN</creatorcontrib><creatorcontrib>SHANG JUNCHENG</creatorcontrib><title>Wheat scab spore identification method based on YOLOv4-tiny lightweight model</title><description>The invention relates to a wheat gibberellic disease spore identification method based on a YOLOv4-tiny lightweight model. Compared with the prior art, the wheat gibberellic disease spore identification method solves the defects that gibberellic disease spore detection is low in accuracy, low in detection speed and large in calculation amount. The method comprises the following steps: establishing a spore image data set; constructing a lightweight spore recognition model; training a lightweight spore recognition model; acquiring a spore image to be identified; and obtaining a wheat scab spore identification result. According to the method, the characteristics of higher detection speed and better real-time performance of YOLOv4-tin are utilized, and rapid detection is realized while the accuracy is ensured; according to the method, the CSPDarknet53 module of the YOLOv4 trunk feature extraction network is replaced by the CSPDarknet53tiny module, so that rapid and accurate detection and identification of the whe</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>eNrjZPANz0hNLFEoTk5MUiguyC9KVchMSc0ryUzLTE4syczPU8hNLcnIT1FISixOTVEA8iP9ffzLTHRLMvMqFXIy0zNKylNBpEJufkpqDg8Da1piTnEqL5TmZlB0cw1x9tBNLciPTy0uSExOzUstiXf2MzQ0N7Q0tzAwdzQmRg0Ap4w11w</recordid><startdate>20231208</startdate><enddate>20231208</enddate><creator>ZHANG HANSU</creator><creator>CHANG HAOYU</creator><creator>CHEN XU</creator><creator>SHIM CHAN-JOON</creator><creator>LI SHICHANG</creator><creator>ZHANG SHENGYU</creator><creator>ZHAN ZICHAO</creator><creator>HOU ZHIMENG</creator><creator>LI HAIDONG</creator><creator>YU SHUAN</creator><creator>SHANG JUNCHENG</creator><scope>EVB</scope></search><sort><creationdate>20231208</creationdate><title>Wheat scab spore identification method based on YOLOv4-tiny lightweight model</title><author>ZHANG HANSU ; CHANG HAOYU ; CHEN XU ; SHIM CHAN-JOON ; LI SHICHANG ; ZHANG SHENGYU ; ZHAN ZICHAO ; HOU ZHIMENG ; LI HAIDONG ; YU SHUAN ; SHANG JUNCHENG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN117197807A3</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>ZHANG HANSU</creatorcontrib><creatorcontrib>CHANG HAOYU</creatorcontrib><creatorcontrib>CHEN XU</creatorcontrib><creatorcontrib>SHIM CHAN-JOON</creatorcontrib><creatorcontrib>LI SHICHANG</creatorcontrib><creatorcontrib>ZHANG SHENGYU</creatorcontrib><creatorcontrib>ZHAN ZICHAO</creatorcontrib><creatorcontrib>HOU ZHIMENG</creatorcontrib><creatorcontrib>LI HAIDONG</creatorcontrib><creatorcontrib>YU SHUAN</creatorcontrib><creatorcontrib>SHANG JUNCHENG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHANG HANSU</au><au>CHANG HAOYU</au><au>CHEN XU</au><au>SHIM CHAN-JOON</au><au>LI SHICHANG</au><au>ZHANG SHENGYU</au><au>ZHAN ZICHAO</au><au>HOU ZHIMENG</au><au>LI HAIDONG</au><au>YU SHUAN</au><au>SHANG JUNCHENG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Wheat scab spore identification method based on YOLOv4-tiny lightweight model</title><date>2023-12-08</date><risdate>2023</risdate><abstract>The invention relates to a wheat gibberellic disease spore identification method based on a YOLOv4-tiny lightweight model. Compared with the prior art, the wheat gibberellic disease spore identification method solves the defects that gibberellic disease spore detection is low in accuracy, low in detection speed and large in calculation amount. The method comprises the following steps: establishing a spore image data set; constructing a lightweight spore recognition model; training a lightweight spore recognition model; acquiring a spore image to be identified; and obtaining a wheat scab spore identification result. According to the method, the characteristics of higher detection speed and better real-time performance of YOLOv4-tin are utilized, and rapid detection is realized while the accuracy is ensured; according to the method, the CSPDarknet53 module of the YOLOv4 trunk feature extraction network is replaced by the CSPDarknet53tiny module, so that rapid and accurate detection and identification of the whe</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN117197807A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | Wheat scab spore identification method based on YOLOv4-tiny lightweight model |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T06%3A29%3A49IST&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=ZHANG%20HANSU&rft.date=2023-12-08&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN117197807A%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 |