A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data
The surface defect detection method based on visual perception has been widely used in industrial quality inspection. Because defect data are not easy to obtain and the annotation of a large number of defect data will waste a lot of manpower and material resources. Therefore, this paper reviews the...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Artikel |
| Sprache: | 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 | Jin, Qifan Chen, Li |
| description | The surface defect detection method based on visual perception has been
widely used in industrial quality inspection. Because defect data are not easy
to obtain and the annotation of a large number of defect data will waste a lot
of manpower and material resources. Therefore, this paper reviews the methods
of surface defect detection of industrial products based on a small number of
labeled data, and this method is divided into traditional image
processing-based industrial product surface defect detection methods and deep
learning-based industrial product surface defect detection methods suitable for
a small number of labeled data. The traditional image processing-based
industrial product surface defect detection methods are divided into
statistical methods, spectral methods and model methods. Deep learning-based
industrial product surface defect detection methods suitable for a small number
of labeled data are divided into based on data augmentation, based on transfer
learning, model-based fine-tuning, semi-supervised, weak supervised and
unsupervised. |
| doi_str_mv | 10.48550/arxiv.2203.05733 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2203_05733</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2203_05733</sourcerecordid><originalsourceid>FETCH-LOGICAL-a673-81df9e672f3681290080ffae6f8e51ef2a68af2d22ad312cdd0dd7136109c22e3</originalsourceid><addsrcrecordid>eNotj81OwzAQBn3hgAoPwAm_QIK9Jo5zLC0_lSJAovdoa-9KkZIGOU5F356kcJqVZvVJI8SdVvmjKwr1gPGnPeUAyuSqKI25Fn4tv6Z4orMceLkYPcktMfk0I81oh-PidscwjSm22MnPOITJp1E-4UhBzn4e6bHr5PvUHygu7zUeqJvlFhPeiCvGbqTbf67E_uV5v3nL6o_X3WZdZ2hLkzkduCJbAhvrNFRKOcWMZNlRoYkBrUOGAIDBaPAhqBBKbaxWlQcgsxL3f7OXyuY7tj3Gc7PUNpda8wvSaU79</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data</title><source>arXiv.org</source><creator>Jin, Qifan ; Chen, Li</creator><creatorcontrib>Jin, Qifan ; Chen, Li</creatorcontrib><description>The surface defect detection method based on visual perception has been
widely used in industrial quality inspection. Because defect data are not easy
to obtain and the annotation of a large number of defect data will waste a lot
of manpower and material resources. Therefore, this paper reviews the methods
of surface defect detection of industrial products based on a small number of
labeled data, and this method is divided into traditional image
processing-based industrial product surface defect detection methods and deep
learning-based industrial product surface defect detection methods suitable for
a small number of labeled data. The traditional image processing-based
industrial product surface defect detection methods are divided into
statistical methods, spectral methods and model methods. Deep learning-based
industrial product surface defect detection methods suitable for a small number
of labeled data are divided into based on data augmentation, based on transfer
learning, model-based fine-tuning, semi-supervised, weak supervised and
unsupervised.</description><identifier>DOI: 10.48550/arxiv.2203.05733</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2022-03</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,781,886</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2203.05733$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2203.05733$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Jin, Qifan</creatorcontrib><creatorcontrib>Chen, Li</creatorcontrib><title>A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data</title><description>The surface defect detection method based on visual perception has been
widely used in industrial quality inspection. Because defect data are not easy
to obtain and the annotation of a large number of defect data will waste a lot
of manpower and material resources. Therefore, this paper reviews the methods
of surface defect detection of industrial products based on a small number of
labeled data, and this method is divided into traditional image
processing-based industrial product surface defect detection methods and deep
learning-based industrial product surface defect detection methods suitable for
a small number of labeled data. The traditional image processing-based
industrial product surface defect detection methods are divided into
statistical methods, spectral methods and model methods. Deep learning-based
industrial product surface defect detection methods suitable for a small number
of labeled data are divided into based on data augmentation, based on transfer
learning, model-based fine-tuning, semi-supervised, weak supervised and
unsupervised.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj81OwzAQBn3hgAoPwAm_QIK9Jo5zLC0_lSJAovdoa-9KkZIGOU5F356kcJqVZvVJI8SdVvmjKwr1gPGnPeUAyuSqKI25Fn4tv6Z4orMceLkYPcktMfk0I81oh-PidscwjSm22MnPOITJp1E-4UhBzn4e6bHr5PvUHygu7zUeqJvlFhPeiCvGbqTbf67E_uV5v3nL6o_X3WZdZ2hLkzkduCJbAhvrNFRKOcWMZNlRoYkBrUOGAIDBaPAhqBBKbaxWlQcgsxL3f7OXyuY7tj3Gc7PUNpda8wvSaU79</recordid><startdate>20220310</startdate><enddate>20220310</enddate><creator>Jin, Qifan</creator><creator>Chen, Li</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20220310</creationdate><title>A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data</title><author>Jin, Qifan ; Chen, Li</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a673-81df9e672f3681290080ffae6f8e51ef2a68af2d22ad312cdd0dd7136109c22e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Jin, Qifan</creatorcontrib><creatorcontrib>Chen, Li</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jin, Qifan</au><au>Chen, Li</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data</atitle><date>2022-03-10</date><risdate>2022</risdate><abstract>The surface defect detection method based on visual perception has been
widely used in industrial quality inspection. Because defect data are not easy
to obtain and the annotation of a large number of defect data will waste a lot
of manpower and material resources. Therefore, this paper reviews the methods
of surface defect detection of industrial products based on a small number of
labeled data, and this method is divided into traditional image
processing-based industrial product surface defect detection methods and deep
learning-based industrial product surface defect detection methods suitable for
a small number of labeled data. The traditional image processing-based
industrial product surface defect detection methods are divided into
statistical methods, spectral methods and model methods. Deep learning-based
industrial product surface defect detection methods suitable for a small number
of labeled data are divided into based on data augmentation, based on transfer
learning, model-based fine-tuning, semi-supervised, weak supervised and
unsupervised.</abstract><doi>10.48550/arxiv.2203.05733</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2203.05733 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2203_05733 |
| source | arXiv.org |
| subjects | Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition |
| title | A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T06%3A15%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Survey%20of%20Surface%20Defect%20Detection%20of%20Industrial%20Products%20Based%20on%20A%20Small%20Number%20of%20Labeled%20Data&rft.au=Jin,%20Qifan&rft.date=2022-03-10&rft_id=info:doi/10.48550/arxiv.2203.05733&rft_dat=%3Carxiv_GOX%3E2203_05733%3C/arxiv_GOX%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 |