Colour‐patterned fabric defect detection based on an unsupervised multi‐scale U‐shaped denoising convolutional autoencoder model

This study proposes an unsupervised, learning‐based, reconstructed scheme and a residual analysis‐based defect detection model for colour‐patterned fabric defect detection problems in the clothing process industry. It solves the challenging problems of existing supervised fabric defect detection met...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Coloration technology Ročník 138; číslo 5; s. 522 - 537
Hlavní autoři:	Zhang, Hongwei, Liu, Shuting, Tan, Quanlu, Lu, Shuai, Yao, Le, Ge, Zhiqiang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bradford Wiley Subscription Services, Inc 01.10.2022
Témata:	Color Defects Image reconstruction Noise reduction
ISSN:	1472-3581, 1478-4408
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	This study proposes an unsupervised, learning‐based, reconstructed scheme and a residual analysis‐based defect detection model for colour‐patterned fabric defect detection problems in the clothing process industry. It solves the challenging problems of existing supervised fabric defect detection methods, such as high costs in manually labelling samples and designing features, unstable generalisation ability and scarcity of defective samples. First, for a specific texture, the training set was constructed by collecting easily accessible defect‐free colour‐patterned fabric images. Second, a multi‐scale U‐shaped denoising convolutional autoencoder was modelled using defect‐free samples, which can reconstruct the newly tested colour‐patterned fabric images automatically. Subsequently, a residual map between the original image and corresponding reconstructed image was calculated. Finally, the defective areas were detected and accurately localised by further opening operations. The experimental results indicated that the proposed method is valid and robust for detecting defects in various colour‐patterned fabrics. Moreover, with the YDFID‐1 dataset, compared with other models, the intersection over union index of the model proposed in the current paper was improved by at least 3.95%.
AbstractList	This study proposes an unsupervised, learning‐based, reconstructed scheme and a residual analysis‐based defect detection model for colour‐patterned fabric defect detection problems in the clothing process industry. It solves the challenging problems of existing supervised fabric defect detection methods, such as high costs in manually labelling samples and designing features, unstable generalisation ability and scarcity of defective samples. First, for a specific texture, the training set was constructed by collecting easily accessible defect‐free colour‐patterned fabric images. Second, a multi‐scale U‐shaped denoising convolutional autoencoder was modelled using defect‐free samples, which can reconstruct the newly tested colour‐patterned fabric images automatically. Subsequently, a residual map between the original image and corresponding reconstructed image was calculated. Finally, the defective areas were detected and accurately localised by further opening operations. The experimental results indicated that the proposed method is valid and robust for detecting defects in various colour‐patterned fabrics. Moreover, with the YDFID‐1 dataset, compared with other models, the intersection over union index of the model proposed in the current paper was improved by at least 3.95%.
Author	Lu, Shuai Liu, Shuting Yao, Le Zhang, Hongwei Ge, Zhiqiang Tan, Quanlu
Author_xml	– sequence: 1 givenname: Hongwei surname: Zhang fullname: Zhang, Hongwei email: zhanghongwei@zju.edu.cn organization: Xi'an Polytechnic University – sequence: 2 givenname: Shuting surname: Liu fullname: Liu, Shuting organization: Xi'an Polytechnic University – sequence: 3 givenname: Quanlu orcidid: 0000-0002-9152-5237 surname: Tan fullname: Tan, Quanlu organization: Xi'an Polytechnic University – sequence: 4 givenname: Shuai surname: Lu fullname: Lu, Shuai organization: Beijing Institute of Technology – sequence: 5 givenname: Le surname: Yao fullname: Yao, Le organization: Institute of Industrial Process Control, College of Control Science and Engineering, Zhejiang University – sequence: 6 givenname: Zhiqiang surname: Ge fullname: Ge, Zhiqiang organization: Institute of Industrial Process Control, College of Control Science and Engineering, Zhejiang University
BookMark	eNp9UL1OwzAYtFCRKIWFJ4jEhpTin6RJRlSVH6lSl3aOvjgOuHLtYDtF3ZiYeUaeBKdhQggPvpPv7tPnO0cjbbRA6IrgKQnnlhsvpoTOcHGCxiTJ8jhJcD46chqzNCdn6Ny5LcYU5wkbo4-5UaazX--fLXgvrBZ11EBlJY9q0QjuA_gA0uioAhfUQEBHnXZdK-xe9k-7TnkZRjgOSkSbnr1AG4RaaCOd1M8RN3pvVNfPARVB543Q3NTCRrtwqwt02oBy4vIHJ2hzv1jPH-Pl6uFpfreMOcOkiIERmqaMVDmtIOV5g6samoyQXNS0yrKGpZTX9YxyzBPepElQAOOEF0WVMQA2QdfD3Naa1044X27D78NKrqQZSSjBM0aCCw8ubo1zVjQllx763b0FqUqCy77tsm-7PLYdIje_Iq2VO7CHv81kML9JJQ7_OMv5ar0YMt840Zil
CitedBy_id	crossref_primary_10_1016_j_engappai_2025_111708 crossref_primary_10_3390_pr11092615 crossref_primary_10_3390_app14156774
Cites_doi	10.1109/TII.2020.3010562 10.1177/0040517509340599 10.1109/JBHI.2019.2891526 10.1515/aut-2015-0001 10.3390/app9173506 10.1007/s00371-020-01820-w 10.1109/TIE.2018.2856200 10.1177/1558925020908268 10.1177/0040517519840636 10.1007/978-3-319-24574-4_28 10.1109/ACCESS.2018.2868059 10.1109/TIE.1930.896476 10.1007/s11042-010-0472-8 10.1109/ACCESS.2020.3021189 10.1109/CVPR.2019.00982 10.1109/TASE.2016.2520955 10.1117/1.OE.56.9.093104 10.1155/2016/9794723 10.1002/mrm.28111 10.1016/j.patcog.2007.11.014 10.1007/s00521-016-2645-5 10.3390/rs11242970 10.2478/aut-2019-0035 10.1111/coin.12206 10.3390/s18041064 10.1016/j.ijleo.2016.09.110 10.5220/0007364500002108 10.1108/IJCST-10-2015-0117 10.1109/TII.2018.2809730 10.1016/j.asoc.2019.105489
ContentType	Journal Article
Copyright	2022 Society of Dyers and Colourists. 2022 Society of Dyers and Colourists
Copyright_xml	– notice: 2022 Society of Dyers and Colourists. – notice: 2022 Society of Dyers and Colourists
DBID	AAYXX CITATION 7TB 8FD F28 FR3
DOI	10.1111/cote.12609
DatabaseName	CrossRef Mechanical & Transportation Engineering Abstracts Technology Research Database ANTE: Abstracts in New Technology & Engineering Engineering Research Database
DatabaseTitle	CrossRef Engineering Research Database Technology Research Database Mechanical & Transportation Engineering Abstracts ANTE: Abstracts in New Technology & Engineering
DatabaseTitleList	CrossRef Engineering Research Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1478-4408
EndPage	537
ExternalDocumentID	10_1111_cote_12609 COTE12609
Genre	article
GrantInformation_xml	– fundername: the Natural Science Foundation of Shaanxi Province under Grant 2019JM‐263 – fundername: the Graduate Scientific Innovation Fund for Xi'an Polytechnic University under Grant chx2021015 – fundername: State Key Laboratory of Industrial Control Technology – fundername: National Natural Science Foundation of China – fundername: the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (No. ICT2021B04) – fundername: Zhejiang University – fundername: the Key R&D Plan of Shaanxi Province under Grant 2019ZDLGY01‐08 – fundername: Xi'an Polytechnic University – fundername: Innovation Fund – fundername: Natural Science Foundation of Shaanxi Province
GroupedDBID	.3N .GA .Y3 05W 0B8 0R~ 10A 1OC 29F 31~ 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5HH 5LA 5VS 66C 6J9 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AABCJ AAESR AAEVG AAHHS AAHQN AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABDPE ABEML ABJNI ABPVW ACAHQ ACBWZ ACCFJ ACCZN ACGFO ACGFS ACIWK ACPOU ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADNMO ADOZA ADXAS ADZMN ADZOD AEEZP AEGXH AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFFPM AFGKR AFPWT AFWVQ AFZJQ AHBTC AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CAG COF CS3 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 EBS EJD F00 F01 F04 FEDTE G-S G.N GODZA H.T H.X HF~ HGLYW HVGLF HZI HZ~ I-F IX1 J0M LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ O66 O9- OIG P2W P2X P4D Q.N Q11 QB0 R.K RJQFR ROL RX1 SUPJJ TTC UB1 ULE V8K W8V W99 WBFHL WBKPD WIH WIK WOHZO WQJ WRC WXSBR WYISQ XG1 XJN XOL ZZTAW ~IA ~WT AAMMB AAYXX ADMLS AEFGJ AEYWJ AGHNM AGQPQ AGXDD AGYGG AIDQK AIDYY AIQQE CITATION O8X 7TB 8FD F28 FR3
ID	FETCH-LOGICAL-c3019-a3125531b82ba5c8f0bdaf7118ed2b77f352cdd62c0c4cf54118a004c99b73aa3
IEDL.DBID	DRFUL
ISICitedReferencesCount	3
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000789216800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1472-3581
IngestDate	Sun Nov 30 04:26:32 EST 2025 Sat Nov 29 04:26:04 EST 2025 Tue Nov 18 21:07:08 EST 2025 Wed Jan 22 16:24:19 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	5
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c3019-a3125531b82ba5c8f0bdaf7118ed2b77f352cdd62c0c4cf54118a004c99b73aa3
Notes	Funding information the Graduate Scientific Innovation Fund for Xi'an Polytechnic University under Grant chx2021015; the Key R&D Plan of Shaanxi Province under Grant 2019ZDLGY01‐08; the National Natural Science Foundation of China under Grant 61803292; the Natural Science Foundation of Shaanxi Province under Grant 2019JM‐263; the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (No. ICT2021B04); Xi'an Polytechnic University; Innovation Fund; Natural Science Foundation of Shaanxi Province; Zhejiang University; State Key Laboratory of Industrial Control Technology; National Natural Science Foundation of China ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-9152-5237
PQID	2714210631
PQPubID	2045174
PageCount	16
ParticipantIDs	proquest_journals_2714210631 crossref_citationtrail_10_1111_cote_12609 crossref_primary_10_1111_cote_12609 wiley_primary_10_1111_cote_12609_COTE12609
PublicationCentury	2000
PublicationDate	October 2022 2022-10-00 20221001
PublicationDateYYYYMMDD	2022-10-01
PublicationDate_xml	– month: 10 year: 2022 text: October 2022
PublicationDecade	2020
PublicationPlace	Bradford
PublicationPlace_xml	– name: Bradford
PublicationTitle	Coloration technology
PublicationYear	2022
Publisher	Wiley Subscription Services, Inc
Publisher_xml	– name: Wiley Subscription Services, Inc
References	2017; 5 2015; 15 2018; 29 2019; 9 2021; 67 2019; 11 2020; 84 2019; 32 2019; 35 2020; 17 2011; 52 2020; 37 2020; 15 2019; 19 2016; 2016 2008; 55 2016; 127 2010; 80 2020; 8 2018; 6 2018; 18 2019; 81 2017; 14 2019; 66 2019; 89 2019; 23 2017; 56 2019 2017 2015 2008; 41 2016; 28 2018; 14 e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_3_1 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_14_1 e_1_2_7_13_1 e_1_2_7_12_1 e_1_2_7_10_1 e_1_2_7_26_1 e_1_2_7_27_1 e_1_2_7_29_1 Schlegl T (e_1_2_7_35_1) 2017 Aslam MS (e_1_2_7_28_1) 2021; 67 Zhang HW (e_1_2_7_22_1) 2019; 32 e_1_2_7_30_1 e_1_2_7_25_1 e_1_2_7_31_1 e_1_2_7_24_1 e_1_2_7_32_1 e_1_2_7_23_1 e_1_2_7_33_1 e_1_2_7_34_1 e_1_2_7_21_1 e_1_2_7_20_1 Tong L (e_1_2_7_11_1) 2017; 5
References_xml	– volume: 80 start-page: 579 issue: 7 year: 2010 end-page: 589 article-title: Detection of fabric defects by auto‐regressive spectral analysis and support vector data daescription publication-title: Text. Res. J. – start-page: 234 year: 2015 end-page: 241 – volume: 14 start-page: 3235 issue: 7 year: 2018 end-page: 3243 article-title: Deep learning‐based feature representation and its application for soft sensor modeling with variable‐wise weighted sae publication-title: IEEE Trans Industr Inform – volume: 6 start-page: 49170 year: 2018 end-page: 49181 article-title: Fabric defect detection using salience metric for color dissimilarity and positional aggregation publication-title: IEEE Access – volume: 17 start-page: 6399 issue: 9 year: 2020 end-page: 6408 article-title: Industrial big data modeling and monitoring framework for plant‐wide processes publication-title: IEEE Trans. Industr. Inform. – volume: 18 start-page: 1064 year: 2018 article-title: Automatic fabric defect detection with a multi‐scale convolutional denoising autoencoder network model publication-title: Sensors – volume: 37 start-page: 515 issue: 3 year: 2020 end-page: 528 article-title: Fabric defect detection based on information entropy and frequency domain saliency publication-title: Vis Comput – volume: 52 start-page: 147 issue: 1 year: 2011 end-page: 157 article-title: Fabric defect detection using local contrast deviations publication-title: Multimed Tools Appl – volume: 81 start-page: 105489 year: 2019 article-title: On improving the accuracy with auto‐encoder on conjunctivitis[J] publication-title: Appl Soft Comput – volume: 23 start-page: 1363 issue: 4 year: 2019 end-page: 1373 article-title: Knowledge‐aided convolutional neural network for small organ segmentation[J] publication-title: IEEE J Biomed Health Inform – volume: 9 start-page: 3506 issue: 17 year: 2019 article-title: Fabric defect detection using l0 gradient minimization and fuzzy c‐means publication-title: Appl Sci‐Basel – volume: 56 issue: 9 year: 2017 article-title: Yarn‐dyed fabric defect classification based on convolutional neural network[J] publication-title: Opt Eng – volume: 29 start-page: 1285 issue: 12 year: 2018 end-page: 1307 article-title: Multi‐level image thresholding using otsu and chaotic bat algorithm publication-title: Neural Comput Appl – volume: 15 start-page: 226 issue: 3 year: 2015 end-page: 232 article-title: Yarn‐dyed fabric defect detection based on autocorrelation function and glcm publication-title: Autex Res. J. – volume: 35 start-page: 517 issue: 3 year: 2019 end-page: 534 article-title: Fabric defect detection based on saliency histogram features publication-title: Comput Intell – volume: 5 start-page: 5947 year: 2017 end-page: 5964 article-title: Fabric defect detection for apparel industry: a nonlocal sparse representation approach publication-title: IEEE Access – volume: 67 start-page: 1899 issue: 2 year: 2021 end-page: 1914 article-title: Liver‐tumor detection using CNN ResUNet publication-title: Comp Mater Contin – volume: 84 start-page: 437 issue: 1 year: 2020 end-page: 449 article-title: Automated cartilage and meniscus segmentation of knee mri with conditional generative adversarial networks publication-title: Magn Reson Med – volume: 19 start-page: 363 issue: 4 year: 2019 end-page: 374 article-title: A public fabric database for defect detection methods and results publication-title: Autex Res J – volume: 2016 start-page: 9794723 year: 2016 end-page: 9794728 article-title: Using the dual‐tree complex wavelet transform for improved fabric defect detection publication-title: J Sens – volume: 8 start-page: 161317 year: 2020 end-page: 161325 article-title: Pixel‐wise fabric defect detection by cnns without labeled training data publication-title: IEEE Access – volume: 127 start-page: 11960 issue: 24 year: 2016 end-page: 11973 article-title: Fabric defect detection systems and methods‐a systematic literature review publication-title: Optik – start-page: 9592 year: 2019 end-page: 9600 – volume: 14 start-page: 1256 issue: 2 year: 2017 end-page: 1264 article-title: Deformable patterned fabric defect detection with fisher criterion‐based deep learning publication-title: IEEE Trans Autom Sci Eng – volume: 55 start-page: 348 issue: 1 year: 2008 end-page: 363 article-title: Computer‐vision‐based fabric defect detection: a survey publication-title: IEEE Trans. Ind. Electron. – volume: 28 start-page: 516 issue: 4 year: 2016 end-page: 529 article-title: Detection of varied defects in diverse fabric images via modified rpca with noise term and defect prior publication-title: Int J Clot Sci Technol – volume: 32 start-page: 119 issue: 2 year: 2019 end-page: 125 article-title: Defect detection and location fo yarn‐dyed shirt piece based on denoising convolutional autoencoder publication-title: Basic Sciences Journal of Textile Universities – volume: 89 start-page: 4766 issue: 21 year: 2019 end-page: 4793 article-title: A universal defect detection approach for various types of fabrics based on the elo‐rating algorithm of the integral image publication-title: Text Res J – volume: 41 start-page: 1878 issue: 6 year: 2008 end-page: 1894 article-title: Motif‐based defect detection for patterned fabric publication-title: Pattern Recognit – volume: 15 start-page: 1558925020908268 year: 2020 article-title: Fabric defect detection using the improved yolov3 model publication-title: J Eng Fibers Fabr – volume: 11 start-page: 2970 issue: 24 year: 2019 article-title: Building extraction from very high resolution aerial imagery using joint attention deep neural network publication-title: Remote Sens (Basel) – volume: 66 start-page: 3681 issue: 5 year: 2019 end-page: 3692 article-title: Scalable semisupervised gmm for big data quality prediction in multimode processes publication-title: IEEE Trans Ind Electron – start-page: 146 year: 2017 end-page: 157 – ident: e_1_2_7_31_1 doi: 10.1109/TII.2020.3010562 – ident: e_1_2_7_12_1 doi: 10.1177/0040517509340599 – ident: e_1_2_7_26_1 doi: 10.1109/JBHI.2019.2891526 – ident: e_1_2_7_2_1 doi: 10.1515/aut-2015-0001 – ident: e_1_2_7_7_1 doi: 10.3390/app9173506 – ident: e_1_2_7_10_1 doi: 10.1007/s00371-020-01820-w – ident: e_1_2_7_19_1 doi: 10.1109/TIE.2018.2856200 – ident: e_1_2_7_20_1 doi: 10.1177/1558925020908268 – start-page: 146 volume-title: Unsupervised Anomaly Detection with Generative. Adversarial Networks to Guide Marker Discovery[C]//International Conference on Information Processing in Medical Imaging year: 2017 ident: e_1_2_7_35_1 – ident: e_1_2_7_6_1 doi: 10.1177/0040517519840636 – ident: e_1_2_7_27_1 doi: 10.1007/978-3-319-24574-4_28 – ident: e_1_2_7_4_1 doi: 10.1109/ACCESS.2018.2868059 – ident: e_1_2_7_13_1 doi: 10.1109/TIE.1930.896476 – ident: e_1_2_7_14_1 doi: 10.1007/s11042-010-0472-8 – ident: e_1_2_7_24_1 doi: 10.1109/ACCESS.2020.3021189 – ident: e_1_2_7_32_1 doi: 10.1109/CVPR.2019.00982 – ident: e_1_2_7_5_1 doi: 10.1109/TASE.2016.2520955 – volume: 67 start-page: 1899 issue: 2 year: 2021 ident: e_1_2_7_28_1 article-title: Liver‐tumor detection using CNN ResUNet publication-title: Comp Mater Contin – ident: e_1_2_7_18_1 doi: 10.1117/1.OE.56.9.093104 – ident: e_1_2_7_9_1 doi: 10.1155/2016/9794723 – ident: e_1_2_7_30_1 doi: 10.1002/mrm.28111 – ident: e_1_2_7_3_1 doi: 10.1016/j.patcog.2007.11.014 – ident: e_1_2_7_33_1 doi: 10.1007/s00521-016-2645-5 – ident: e_1_2_7_29_1 doi: 10.3390/rs11242970 – ident: e_1_2_7_34_1 doi: 10.2478/aut-2019-0035 – ident: e_1_2_7_16_1 doi: 10.1111/coin.12206 – ident: e_1_2_7_23_1 doi: 10.3390/s18041064 – ident: e_1_2_7_15_1 doi: 10.1016/j.ijleo.2016.09.110 – ident: e_1_2_7_21_1 doi: 10.5220/0007364500002108 – volume: 5 start-page: 5947 year: 2017 ident: e_1_2_7_11_1 article-title: Fabric defect detection for apparel industry: a nonlocal sparse representation approach publication-title: IEEE Access – ident: e_1_2_7_8_1 doi: 10.1108/IJCST-10-2015-0117 – ident: e_1_2_7_17_1 doi: 10.1109/TII.2018.2809730 – volume: 32 start-page: 119 issue: 2 year: 2019 ident: e_1_2_7_22_1 article-title: Defect detection and location fo yarn‐dyed shirt piece based on denoising convolutional autoencoder publication-title: Basic Sciences Journal of Textile Universities – ident: e_1_2_7_25_1 doi: 10.1016/j.asoc.2019.105489
SSID	ssj0020843
Score	2.2997642
Snippet	This study proposes an unsupervised, learning‐based, reconstructed scheme and a residual analysis‐based defect detection model for colour‐patterned fabric...
SourceID	proquest crossref wiley
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	522
SubjectTerms	Color Defects Image reconstruction Noise reduction
Title	Colour‐patterned fabric defect detection based on an unsupervised multi‐scale U‐shaped denoising convolutional autoencoder model
URI	https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fcote.12609 https://www.proquest.com/docview/2714210631
Volume	138
WOSCitedRecordID	wos000789216800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVWIB databaseName: Wiley Online Library - Journals customDbUrl: eissn: 1478-4408 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0020843 issn: 1472-3581 databaseCode: DRFUL dateStart: 19990101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEB58HfTgW6wvFvSiEEk3bTcBL1JbPEgVaaW3sK-gIGlpEs-ePPsb_SXObNNaQQTxlCW7CcvOzjczu9_OApwo2sqzDe4lvlYeWSgvsmHimVCi_UObod2O6cON6HTCfj-6m4OLyVmYcX6I6YIbaYbDa1JwqbIZJdeD3J5X0R2P5mGRTlVh6LV4dd_u3UwDLj8cE-xrghjrYbVMT0pMnq-vvxukLy9z1ld1xqa99r9ursNq6WSyy_Gs2IA5m27CykzqwS14ayLqFaOP1_ehy7CJaMsSqRAVmbFE8cBH7mhaKSNLZxgWZMqKNCuGhC_0yrER8RcZCtqyHpUe5RArEMwGT7QKwYjVXs5u7JEs8gFlzjR2xNwdPNvQa7e6zWuvvJPB0wgFkScD9IhQb1XIlazrMPGVkYnAMMUaroRI0KHTxjS49nVNJ_Ua1khURB1FSgRSBjuwkA5SuwvMl5zrhAsZKYTrulRoJtEhU4ERgTWSV-B0IphYlwnL6d6M53gSuNDYxm5sK3A8bTscp-n4sdXBRL5xqapZzEW1hnFvI6hW4MxJ8pc_xM3bbsuV9v7SeB-WOR2bcCTAA1jIR4U9hCX9kj9lo6Ny2n4CnhD25Q
linkProvider	Wiley-Blackwell
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fa9swED66dLD2oe22jmZNV8H2soGLLTuW_VjShpa56RjJ6JvRL9PAcEJi73lPfe7f2L9kd4qTZjAKo08WlmyETvfdnfTpBPBJ0VaejblX-Fp5ZKG81CaFZxKJ9g9thnY7pj8yMRgkNzfpt4abQ2dhFvkhVgtupBkOr0nBaUF6Tcv1pLInAfrj6QvYjOJQJC3YPPveH2WriMtPFgz7SBBlPQma_KRE5Xn8-m-L9Ohmrjurztr0d5_Zzz3YadxMdrqYF69hw5ZvYHst-eBbuOsh7tWzh9_3U5djE_GWFVIhLjJjieSBj8oRtUpGts4wLMiS1eW8nhLC0CvHR8RfzFHUlo2odCunWIFwNhnTOgQjXnszv7FHsq4mlDvT2Blzt_Dsw6h_PuxdeM2tDJ5GMEg9GaJPhJqrEq5kVyeFr4wsBAYq1nAlRIEunTYm5trXkS66EdZIVEWdpkqEUobvoFVOSnsAzJec64ILmSoE7K5UaCjRJVOhEaE1krfh81IyuW5SltPNGT_zZehCY5u7sW3Dx1Xb6SJRxz9bdZYCzhtlnedcBBFGvnEYtOGLE-UTf8h718NzV3r_P42P4dXF8CrLs8vB10PY4nSIwlECO9CqZrU9gpf6VzWezz40c_gPUs761Q
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3da9swED_adJTtYe3ajqVfE3QvK7g4sh3ZjyVtaFlIS2lG34w-aWE4JrH3vKc972_cX9I7xUkzGIXSJwtLNkKn-92d9NMJ4IuirTzb5YELtQrIQgWZTV1gUon2D22G9jum3wdiOEzv7rLrhptDZ2Fm-SEWC26kGR6vScFtadySlutxZU866I9nq7AWJ1kSt2Dt7KY_GiwirjCdMexjQZT1tNPkJyUqz9PX_1qkJzdz2Vn11qa_8cp-bsL7xs1kp7N58QFWbLEF75aSD27D7x7iXj35--tP6XNsIt4yJxXiIjOWSB74qDxRq2Bk6wzDgixYXUzrkhCGXnk-Iv5iiqK2bESle1liBcLZ-IHWIRjx2pv5jT2SdTWm3JnGTpi_hWcHRv3z295F0NzKEGgEgyyQEfpEqLkq5UomOnWhMtIJDFSs4UoIhy6dNqbLdahj7ZIYaySqos4yJSIpo4_QKsaF_QQslJxrx4XMFAJ2IhUaSnTJVGREZI3kbfg6l0yum5TldHPGj3weutDY5n5s23C0aFvOEnX8t9X-XMB5o6zTnItOjJFvN-q04diL8pk_5L2r23Nf2n1J48-wfn3WzweXw2978JbTGQrPCNyHVjWp7QG80T-rh-nksJnCj-hL-lA
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Colour%E2%80%90patterned+fabric+defect+detection+based+on+an+unsupervised+multi%E2%80%90scale+U%E2%80%90shaped+denoising+convolutional+autoencoder+model&rft.jtitle=Coloration+technology&rft.au=Zhang%2C+Hongwei&rft.au=Liu%2C+Shuting&rft.au=Tan%2C+Quanlu&rft.au=Lu%2C+Shuai&rft.date=2022-10-01&rft.pub=Wiley+Subscription+Services%2C+Inc&rft.issn=1472-3581&rft.eissn=1478-4408&rft.volume=138&rft.issue=5&rft.spage=522&rft.epage=537&rft_id=info:doi/10.1111%2Fcote.12609&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1472-3581&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1472-3581&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1472-3581&client=summon