Attention-based vector quantisation variational autoencoder for colour-patterned fabrics defect detection / Hongwei Zhang in COLORATION TECHNOLOGY, Vol. 139, N° 3 (06/2023)  

[article]  inCOLORATION TECHNOLOGY > Vol. 139, N° 3 (06/2023) . - p. 223-238 Titre : Attention-based vector quantisation variational autoencoder for colour-patterned fabrics defect detection Type de document : texte imprimé Auteurs : Hongwei Zhang, Auteur ; Guanhua Qiao, Auteur ; Shuting Liu, Auteur ; Yuting Lyu, Auteur ; Le Yao, Auteur ; Zhiqiang Ge, Auteur Année de publication : 2023 Article en page(s) : p. 223-238 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Auto-encodeurs variationnels En apprentissage automatique, un auto-encodeur variationnel (ou VAE de l'anglais variational auto encoder), est une architecture de réseau de neurones artificiels introduite en 2013 par D. Kingma et M. Welling, appartenant aux familles des modèles graphiques probabilistes et des méthodes bayésiennes variationnelles. Les VAE sont souvent rapprochés des autoencodeurs en raison de leur architectures similaires. Leur utilisation et leur formulation mathématiques sont cependant différentes. Les auto-encodeurs variationnels permettent de formuler un problème d'inférence statistique (par exemple, déduire la valeur d'une variable aléatoire à partir d'une autre variable aléatoire) en un problème d'optimisation statistique (c'est-à-dire trouver les valeurs de paramètres qui minimisent une fonction objectif). Ils représentent une fonction associant à une valeur d'entrée une distribution latente multivariée, qui n'est pas directement observée mais déduite depuis un modèle mathématique à partir de la distribution d'autres variables. Bien que ce type de modèle ait été initialement conçu pour l'apprentissage non supervisé, son efficacité a été prouvée pour l'apprentissage semi-supervisé et l'apprentissage supervisé. (Wikipedia) Détection de défauts (Ingénierie) Textiles et tissus -- Défauts Index. décimale : 667.3 Teinture et impression des tissus Résumé : Defect detection is an essential link in the fabric production process. Due to the diversity of patterns and scarcity of defect samples for colour-patterned fabrics, reconstruction-based unsupervised deep learning algorithms have received extensive attention in the field of fabric defect detection. Among them, unsupervised reconstruction models based on variational autoencoders (VAEs) have been shown to be effective. However, there is a problem of posterior collapse in the process of modelling parametric distributions of continuous variables by VAEs. Therefore, VAE-based defect detection methods for colour-patterned fabrics usually produce ambiguous reconstruction results, thereby affecting the defect detection performance. In this article, an attention-based vector quantisation variational autoencoder (AVQ-VAE) is proposed for colour-patterned fabric defect detection. The method adopts autoregressive modelling of discrete variables to avoid the posterior collapse problem of traditional VAEs, and utilises attention mechanism to enhance the feature representation ability of the model. AVQ-VAE consists of encoder, embedding space, decoder and attention mechanism. The encoder is used to map the input image into multiple feature vectors. Vector quantisation in embedding space is used for discretisation and autoregressive modelling of feature vectors. A decoder is used to decode discrete variables into images of the same size as the original input. Furthermore, an attention mechanism is used to capture channel and spatial correlations, which help the model focus on important information by adaptively recalibrating feature maps. Experimental results on public datasets demonstrate that the proposed method is robust and effective for colour-patterned fabric defect detection. Note de contenu : - RELATED WORKS : Unsupervised deep learning based fabric defect detection - VAE-based model - Attention mechanism - THE PROPOSED AVQ-VAE METHOD : AVQ-VAE and training process - Colour-patterned fabric defect detection based on AVQ-VAE - EXPERIMENT : Experimental platform - Datasets - Parameter setting - Evaluation metrics - Defect detection results on dataset YDFID-1 - Defect detection results on anomaly detection dataset MVTec AD - Table 1 : Number of selected YDFID-1 patterns - Table 2 : Defect detection results of six models on colour-patterned fabrics - Table 3 : AUROC results for the three methodsTABLE 4. AUPRO results for the three methods DOI : https://doi.org/10.1111/cote.12644 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12644 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=39534 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
24086	-	Périodique	Bibliothèque principale	Documentaires	Disponible

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]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
23613	-	Périodique	Bibliothèque principale	Documentaires	Disponible

---

1 (1 - 2 / 2)