Colour-patterned fabric defect detection based on an unsupervised multi-scale U-shaped denoising convolutional autoencoder model / Hongwei Zhang in COLORATION TECHNOLOGY, Vol. 138, N° 5 (10/2022)  

[article]  inCOLORATION TECHNOLOGY > Vol. 138, N° 5 (10/2022) . - p. 522-537 Titre : Colour-patterned fabric defect detection based on an unsupervised multi-scale U-shaped denoising convolutional autoencoder model Type de document : texte imprimé Auteurs : Hongwei Zhang, Auteur ; Shuting Liu, Auteur ; Quanlu Tan, Auteur ; Shuai Lu, Auteur ; Le Yao, Auteur ; Zhiqiang Ge, Auteur Année de publication : 2022 Article en page(s) : p. 522-537 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Détection de défauts (Ingénierie) Impression sur étoffes Textiles et tissus -- Défauts Index. décimale : 667.3 Teinture et impression des tissus Résumé : 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%. Note de contenu : - PRELIMINARIES : The traditional U-Net mode - THE PROPOSED METHOD FOR COLOUR-PATTERNED FABRIC DEFECT DETECTION : The UDCAE model - The multi-scale UDCAE model - The model training phase - The model testing phase for defect detection - EXPERIMENTAL RESULTS AND ANALYSIS : Description of datasets - Experimental platform - Evaluation metrics - Qualitative and quantitative performance analyses - Table 1 : The number of colour-patterned fabric samples - Table 2 : The PSNR and SSIM values of reconstructed results with 11 loss functions - Table 3 : Comparison of time consumption of four loss functions in the training and testing phase - Table 4 : The PSNR and SSIM values of reconstructed defect-free results with four models - Table 5 : Comparison of four evaluation indicators of defect detection results with five models - Table 6 : Comparisons of AUROC of defect detection results with six models DOI : https://doi.org/10.1111/cote.12609 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12609 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=38126 [article]

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