Child positioning method, device and equipment based on optimized Yolov7 model and storage medium
The invention relates to the technical field of computer vision, and provides a child positioning method, device and equipment based on an optimized Yov7 model and a storage medium, the optimized Yov7 model is trained, the optimized Yov7 model comprises an input layer, a BackBone layer, a Neck layer...
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 | SU PEIXIAN QIAN ZHENKE WANG WEIWEN WU XIANSONG ZHONG LIN ZHANG YONG CHEN JUN |
| description | The invention relates to the technical field of computer vision, and provides a child positioning method, device and equipment based on an optimized Yov7 model and a storage medium, the optimized Yov7 model is trained, the optimized Yov7 model comprises an input layer, a BackBone layer, a Neck layer and a Head layer, the Neck layer comprises an SPPCSPC module, and the Head layer comprises an SPPCSPC module. The method comprises the following steps: predicting a child in a video image and positioning the child in the video image through a BackBone layer, a Neck layer and a Head layer; through the Sigmoid function, the depth feature images of the BackBone layer and the Neck layer are multiplexed, and the classification label corresponding to the child is activated, so that the child positioning accuracy is improved, and the target child can be more accurately positioned.
本申请涉及计算机视觉技术领域，提供了一种基于优化Yolov7模型的儿童定位方法、装置、设备及存储介质，优化Yolov7模型为训练好的，优化Yolov7模型包括：输入层、BackBone层、Neck层和Head层，Neck层包括SPPCSPC模块，该方法包括：通过BackBone层、N |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN118247478A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN118247478A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN118247478A3</originalsourceid><addsrcrecordid>eNqNjLEKwkAQBdNYiPoPa69FNJC0EhQrKxurcGafycLd7eldUvj1BvEDrIaBYeaZqXuxTEGjJFEvviOH1CtviDFKCzKeCc9BgoNPdDcRTOpJQxIn70luanUsySnDfuuY9GU6TCOWwS2z2cPYiNWPi2x9Ol7r8xZBG8RgWnikpr7kebUryqKsDvt_mg_m_j12</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Child positioning method, device and equipment based on optimized Yolov7 model and storage medium</title><source>esp@cenet</source><creator>SU PEIXIAN ; QIAN ZHENKE ; WANG WEIWEN ; WU XIANSONG ; ZHONG LIN ; ZHANG YONG ; CHEN JUN</creator><creatorcontrib>SU PEIXIAN ; QIAN ZHENKE ; WANG WEIWEN ; WU XIANSONG ; ZHONG LIN ; ZHANG YONG ; CHEN JUN</creatorcontrib><description>The invention relates to the technical field of computer vision, and provides a child positioning method, device and equipment based on an optimized Yov7 model and a storage medium, the optimized Yov7 model is trained, the optimized Yov7 model comprises an input layer, a BackBone layer, a Neck layer and a Head layer, the Neck layer comprises an SPPCSPC module, and the Head layer comprises an SPPCSPC module. The method comprises the following steps: predicting a child in a video image and positioning the child in the video image through a BackBone layer, a Neck layer and a Head layer; through the Sigmoid function, the depth feature images of the BackBone layer and the Neck layer are multiplexed, and the classification label corresponding to the child is activated, so that the child positioning accuracy is improved, and the target child can be more accurately positioned.
本申请涉及计算机视觉技术领域，提供了一种基于优化Yolov7模型的儿童定位方法、装置、设备及存储介质，优化Yolov7模型为训练好的，优化Yolov7模型包括：输入层、BackBone层、Neck层和Head层，Neck层包括SPPCSPC模块，该方法包括：通过BackBone层、N</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=20240625&DB=EPODOC&CC=CN&NR=118247478A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240625&DB=EPODOC&CC=CN&NR=118247478A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SU PEIXIAN</creatorcontrib><creatorcontrib>QIAN ZHENKE</creatorcontrib><creatorcontrib>WANG WEIWEN</creatorcontrib><creatorcontrib>WU XIANSONG</creatorcontrib><creatorcontrib>ZHONG LIN</creatorcontrib><creatorcontrib>ZHANG YONG</creatorcontrib><creatorcontrib>CHEN JUN</creatorcontrib><title>Child positioning method, device and equipment based on optimized Yolov7 model and storage medium</title><description>The invention relates to the technical field of computer vision, and provides a child positioning method, device and equipment based on an optimized Yov7 model and a storage medium, the optimized Yov7 model is trained, the optimized Yov7 model comprises an input layer, a BackBone layer, a Neck layer and a Head layer, the Neck layer comprises an SPPCSPC module, and the Head layer comprises an SPPCSPC module. The method comprises the following steps: predicting a child in a video image and positioning the child in the video image through a BackBone layer, a Neck layer and a Head layer; through the Sigmoid function, the depth feature images of the BackBone layer and the Neck layer are multiplexed, and the classification label corresponding to the child is activated, so that the child positioning accuracy is improved, and the target child can be more accurately positioned.
本申请涉及计算机视觉技术领域，提供了一种基于优化Yolov7模型的儿童定位方法、装置、设备及存储介质，优化Yolov7模型为训练好的，优化Yolov7模型包括：输入层、BackBone层、Neck层和Head层，Neck层包括SPPCSPC模块，该方法包括：通过BackBone层、N</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>eNqNjLEKwkAQBdNYiPoPa69FNJC0EhQrKxurcGafycLd7eldUvj1BvEDrIaBYeaZqXuxTEGjJFEvviOH1CtviDFKCzKeCc9BgoNPdDcRTOpJQxIn70luanUsySnDfuuY9GU6TCOWwS2z2cPYiNWPi2x9Ol7r8xZBG8RgWnikpr7kebUryqKsDvt_mg_m_j12</recordid><startdate>20240625</startdate><enddate>20240625</enddate><creator>SU PEIXIAN</creator><creator>QIAN ZHENKE</creator><creator>WANG WEIWEN</creator><creator>WU XIANSONG</creator><creator>ZHONG LIN</creator><creator>ZHANG YONG</creator><creator>CHEN JUN</creator><scope>EVB</scope></search><sort><creationdate>20240625</creationdate><title>Child positioning method, device and equipment based on optimized Yolov7 model and storage medium</title><author>SU PEIXIAN ; QIAN ZHENKE ; WANG WEIWEN ; WU XIANSONG ; ZHONG LIN ; ZHANG YONG ; CHEN JUN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118247478A3</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>SU PEIXIAN</creatorcontrib><creatorcontrib>QIAN ZHENKE</creatorcontrib><creatorcontrib>WANG WEIWEN</creatorcontrib><creatorcontrib>WU XIANSONG</creatorcontrib><creatorcontrib>ZHONG LIN</creatorcontrib><creatorcontrib>ZHANG YONG</creatorcontrib><creatorcontrib>CHEN JUN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>SU PEIXIAN</au><au>QIAN ZHENKE</au><au>WANG WEIWEN</au><au>WU XIANSONG</au><au>ZHONG LIN</au><au>ZHANG YONG</au><au>CHEN JUN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Child positioning method, device and equipment based on optimized Yolov7 model and storage medium</title><date>2024-06-25</date><risdate>2024</risdate><abstract>The invention relates to the technical field of computer vision, and provides a child positioning method, device and equipment based on an optimized Yov7 model and a storage medium, the optimized Yov7 model is trained, the optimized Yov7 model comprises an input layer, a BackBone layer, a Neck layer and a Head layer, the Neck layer comprises an SPPCSPC module, and the Head layer comprises an SPPCSPC module. The method comprises the following steps: predicting a child in a video image and positioning the child in the video image through a BackBone layer, a Neck layer and a Head layer; through the Sigmoid function, the depth feature images of the BackBone layer and the Neck layer are multiplexed, and the classification label corresponding to the child is activated, so that the child positioning accuracy is improved, and the target child can be more accurately positioned.
本申请涉及计算机视觉技术领域，提供了一种基于优化Yolov7模型的儿童定位方法、装置、设备及存储介质，优化Yolov7模型为训练好的，优化Yolov7模型包括：输入层、BackBone层、Neck层和Head层，Neck层包括SPPCSPC模块，该方法包括：通过BackBone层、N</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN118247478A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
| title | Child positioning method, device and equipment based on optimized Yolov7 model 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=2025-01-31T16%3A12%3A40IST&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=SU%20PEIXIAN&rft.date=2024-06-25&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN118247478A%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 |