Method and device for constructing binary classification anomaly detection

The invention relates to the technical field of computer vision, in particular to a binary classification anomaly detection method and device.Firstly, an open data set is constructed, a binary classification two-way branch model architecture is designed, when the open data set is constructed, a clus...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: LIU YI, LI SHUSHENG, DUAN JINGFENG, LU ZEXING
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 LIU YI
LI SHUSHENG
DUAN JINGFENG
LU ZEXING
description The invention relates to the technical field of computer vision, in particular to a binary classification anomaly detection method and device.Firstly, an open data set is constructed, a binary classification two-way branch model architecture is designed, when the open data set is constructed, a clustering algorithm is used for dividing all normal samples into even number groups which are not intersected with one another, and a binary classification two-way branch model architecture is designed; each group represents one mode of a normal picture, generating a pseudo abnormal sample, and increasing the diversity of abnormal samples; a binary classification double-path branch model architecture adopts a double-path structure and is divided into a basic branch and a correction branch, and the basic branch and the correction branch adopt different data enhancement modes to increase data distribution difference. Compared with the prior art, the method has the advantages that the model can be prevented from overfitt
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN118711012A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN118711012A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN118711012A3</originalsourceid><addsrcrecordid>eNqNi0EKAjEMAHvxIOof4gMEqwe9yqKIoCfvS0xTDdRmaaOwv3cFH-BpYJgZu9OZ7aEBMAcI_BZiiFqANFcrLzLJd7hJxtIDJaxVohCaaB4OfWLqh8uYvmbqRhFT5dmPEzc_7K_NccGdtlw7JM5sbXPxfrvxfulXu_U_zQdVGjWM</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Method and device for constructing binary classification anomaly detection</title><source>esp@cenet</source><creator>LIU YI ; LI SHUSHENG ; DUAN JINGFENG ; LU ZEXING</creator><creatorcontrib>LIU YI ; LI SHUSHENG ; DUAN JINGFENG ; LU ZEXING</creatorcontrib><description>The invention relates to the technical field of computer vision, in particular to a binary classification anomaly detection method and device.Firstly, an open data set is constructed, a binary classification two-way branch model architecture is designed, when the open data set is constructed, a clustering algorithm is used for dividing all normal samples into even number groups which are not intersected with one another, and a binary classification two-way branch model architecture is designed; each group represents one mode of a normal picture, generating a pseudo abnormal sample, and increasing the diversity of abnormal samples; a binary classification double-path branch model architecture adopts a double-path structure and is divided into a basic branch and a correction branch, and the basic branch and the correction branch adopt different data enhancement modes to increase data distribution difference. Compared with the prior art, the method has the advantages that the model can be prevented from overfitt</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; ELECTRIC DIGITAL DATA PROCESSING ; 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&amp;date=20240927&amp;DB=EPODOC&amp;CC=CN&amp;NR=118711012A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20240927&amp;DB=EPODOC&amp;CC=CN&amp;NR=118711012A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LIU YI</creatorcontrib><creatorcontrib>LI SHUSHENG</creatorcontrib><creatorcontrib>DUAN JINGFENG</creatorcontrib><creatorcontrib>LU ZEXING</creatorcontrib><title>Method and device for constructing binary classification anomaly detection</title><description>The invention relates to the technical field of computer vision, in particular to a binary classification anomaly detection method and device.Firstly, an open data set is constructed, a binary classification two-way branch model architecture is designed, when the open data set is constructed, a clustering algorithm is used for dividing all normal samples into even number groups which are not intersected with one another, and a binary classification two-way branch model architecture is designed; each group represents one mode of a normal picture, generating a pseudo abnormal sample, and increasing the diversity of abnormal samples; a binary classification double-path branch model architecture adopts a double-path structure and is divided into a basic branch and a correction branch, and the basic branch and the correction branch adopt different data enhancement modes to increase data distribution difference. Compared with the prior art, the method has the advantages that the model can be prevented from overfitt</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNi0EKAjEMAHvxIOof4gMEqwe9yqKIoCfvS0xTDdRmaaOwv3cFH-BpYJgZu9OZ7aEBMAcI_BZiiFqANFcrLzLJd7hJxtIDJaxVohCaaB4OfWLqh8uYvmbqRhFT5dmPEzc_7K_NccGdtlw7JM5sbXPxfrvxfulXu_U_zQdVGjWM</recordid><startdate>20240927</startdate><enddate>20240927</enddate><creator>LIU YI</creator><creator>LI SHUSHENG</creator><creator>DUAN JINGFENG</creator><creator>LU ZEXING</creator><scope>EVB</scope></search><sort><creationdate>20240927</creationdate><title>Method and device for constructing binary classification anomaly detection</title><author>LIU YI ; LI SHUSHENG ; DUAN JINGFENG ; LU ZEXING</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN118711012A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>LIU YI</creatorcontrib><creatorcontrib>LI SHUSHENG</creatorcontrib><creatorcontrib>DUAN JINGFENG</creatorcontrib><creatorcontrib>LU ZEXING</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LIU YI</au><au>LI SHUSHENG</au><au>DUAN JINGFENG</au><au>LU ZEXING</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Method and device for constructing binary classification anomaly detection</title><date>2024-09-27</date><risdate>2024</risdate><abstract>The invention relates to the technical field of computer vision, in particular to a binary classification anomaly detection method and device.Firstly, an open data set is constructed, a binary classification two-way branch model architecture is designed, when the open data set is constructed, a clustering algorithm is used for dividing all normal samples into even number groups which are not intersected with one another, and a binary classification two-way branch model architecture is designed; each group represents one mode of a normal picture, generating a pseudo abnormal sample, and increasing the diversity of abnormal samples; a binary classification double-path branch model architecture adopts a double-path structure and is divided into a basic branch and a correction branch, and the basic branch and the correction branch adopt different data enhancement modes to increase data distribution difference. Compared with the prior art, the method has the advantages that the model can be prevented from overfitt</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN118711012A
source esp@cenet
subjects CALCULATING
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
title Method and device for constructing binary classification anomaly detection
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-03T18%3A52%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=LIU%20YI&rft.date=2024-09-27&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN118711012A%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