Colour-patterned fabric-defect detection using unsupervised and memorial defect-free features / Hongwei Zhang in COLORATION TECHNOLOGY, Vol. 138, N° 6 (12/2022)  

[article]  inCOLORATION TECHNOLOGY > Vol. 138, N° 6 (12/2022) . - p. 602-620 Titre : Colour-patterned fabric-defect detection using unsupervised and memorial defect-free features Type de document : texte imprimé Auteurs : Hongwei Zhang, Auteur ; Weiwei Zhang, Auteur ; Yang Wang, Auteur ; Shuai Lu, Auteur ; Le Yao, Auteur ; Xia Chen, Auteur Année de publication : 2022 Article en page(s) : p. 602-620 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Automatisation Détection de défauts (Ingénierie) Teinture -- Fibres textiles Textiles et tissus -- Défauts Textiles et tissus imprimés Index. décimale : 667.3 Teinture et impression des tissus Résumé : Automatic colour-patterned fabric-defect detection is essential and challenging in controlling manufacturing quality. Because of the scarcity of defective colour-patterned fabric samples and the imbalance of defect types, an auto-encoder trained with defect-free samples is used. However, the auto-encoder sometimes has weak generalisation ability, leading to mis- or over-detection of defects. An unsupervised and memorising defect-free method is proposed for colour-patterned fabric defects. The method designs a memory-guided quantisation variational auto-encoder-2 model and improves the residual post-processing operation. Specifically, it has three significant characteristics. First, it avoids time-consuming and laborious manually labelled samples and only needs defect-free samples in the training phase. Second, we expect the reconstruction image to be a defect-free image. Thus, memory modules are introduced to encourage the model to memorise the features of defect-free samples, which help to remove the image defect areas at the testing stage. Third, to further improve the detection accuracy, the closing operation is used to deal with the residual image that is calculated between the tested and the corresponding reconstructed image. Extensive experiments on various representative fabric samples demonstrated the effectiveness and superiority of the proposed method. Note de contenu : - RELATED WORK : Fabric-defect detection - Memory networks - METHOD: Network structure - Model training - Model testing - EXPERIMENTAL RESULTS AND ANALYSIS : Experimental platform - Experimental dataset - Evaluation indexes - Implementation details - Qualitative and quantitative analysis - Table 1 : The numbers of defective and non-defective samples in the YDFID-1 dataset - Table 2 : Comparison of detection performance of different threshold parameters and different mathematical morphology processing methods - Table 3 : PSNR and SSIM values of reconstruction results of seven models - Table 4 : Quantitative analysis and comparison of test results of seven models under four evaluation indexes DOI : https://doi.org/10.1111/cote.12624 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12624 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=38375 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
23716	-	Périodique	Bibliothèque principale	Documentaires	Disponible

---

1 (1 - 1 / 1)