Cross-scale defect detection method
The invention relates to a cross-scale defect detection method which comprises the following steps: S1, acquiring surface defect data of an object to be detected, and classifying and defining defects; s2, performing feature extraction on defect-containing data in the original image to obtain cross-s...
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creator | SHAN ZHONGDE WANG JUN PU CHENGHAN GAO CHANGCAI |
description | The invention relates to a cross-scale defect detection method which comprises the following steps: S1, acquiring surface defect data of an object to be detected, and classifying and defining defects; s2, performing feature extraction on defect-containing data in the original image to obtain cross-scale defect edge features; s3, inputting the original image data and the cross-scale defect edge features of the object to be detected into the SwinIDE-merge network model, and extracting high-dimensional defect information; s4, constructing a defect detection model, outputting the high-dimensional defect information to the defect detection model, and detecting a bounding box prediction result and a classification result of the defects; and S5, aiming at the defect detection model of the cross-scale defect, adopting a Wasserstein distance as a loss function, carrying out training and weight updating on the model, and obtaining a final defect detection model. According to the feature extraction method, fewer down-sa |
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According to the feature extraction method, fewer down-sa</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>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=20231121&DB=EPODOC&CC=CN&NR=117094999A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,309,781,886,25569,76552</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20231121&DB=EPODOC&CC=CN&NR=117094999A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>SHAN ZHONGDE</creatorcontrib><creatorcontrib>WANG JUN</creatorcontrib><creatorcontrib>PU CHENGHAN</creatorcontrib><creatorcontrib>GAO CHANGCAI</creatorcontrib><title>Cross-scale defect detection method</title><description>The invention relates to a cross-scale defect detection method which comprises the following steps: S1, acquiring surface defect data of an object to be detected, and classifying and defining defects; s2, performing feature extraction on defect-containing data in the original image to obtain cross-scale defect edge features; s3, inputting the original image data and the cross-scale defect edge features of the object to be detected into the SwinIDE-merge network model, and extracting high-dimensional defect information; s4, constructing a defect detection model, outputting the high-dimensional defect information to the defect detection model, and detecting a bounding box prediction result and a classification result of the defects; and S5, aiming at the defect detection model of the cross-scale defect, adopting a Wasserstein distance as a loss function, carrying out training and weight updating on the model, and obtaining a final defect detection model. According to the feature extraction method, fewer down-sa</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>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZFB2LsovLtYtTk7MSVVISU1LTS4BUiVAKjM_TyE3tSQjP4WHgTUtMac4lRdKczMourmGOHvophbkx6cWFyQmp-allsQ7-xkamhtYmlhaWjoaE6MGANmsJnY</recordid><startdate>20231121</startdate><enddate>20231121</enddate><creator>SHAN ZHONGDE</creator><creator>WANG JUN</creator><creator>PU CHENGHAN</creator><creator>GAO CHANGCAI</creator><scope>EVB</scope></search><sort><creationdate>20231121</creationdate><title>Cross-scale defect detection method</title><author>SHAN ZHONGDE ; WANG JUN ; PU CHENGHAN ; GAO CHANGCAI</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN117094999A3</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>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>SHAN ZHONGDE</creatorcontrib><creatorcontrib>WANG JUN</creatorcontrib><creatorcontrib>PU CHENGHAN</creatorcontrib><creatorcontrib>GAO CHANGCAI</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>SHAN ZHONGDE</au><au>WANG JUN</au><au>PU CHENGHAN</au><au>GAO CHANGCAI</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Cross-scale defect detection method</title><date>2023-11-21</date><risdate>2023</risdate><abstract>The invention relates to a cross-scale defect detection method which comprises the following steps: S1, acquiring surface defect data of an object to be detected, and classifying and defining defects; s2, performing feature extraction on defect-containing data in the original image to obtain cross-scale defect edge features; s3, inputting the original image data and the cross-scale defect edge features of the object to be detected into the SwinIDE-merge network model, and extracting high-dimensional defect information; s4, constructing a defect detection model, outputting the high-dimensional defect information to the defect detection model, and detecting a bounding box prediction result and a classification result of the defects; and S5, aiming at the defect detection model of the cross-scale defect, adopting a Wasserstein distance as a loss function, carrying out training and weight updating on the model, and obtaining a final defect detection model. 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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PHYSICS |
title | Cross-scale defect detection method |
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