A mixed-attention-based multi-scale autoencoder algorithm for fabric defect detection / Hongwei Zhang in COLORATION TECHNOLOGY, Vol. 140, N° 3 (06/2024)  

[article]  inCOLORATION TECHNOLOGY > Vol. 140, N° 3 (06/2024) . - p. 451-466 Titre : A mixed-attention-based multi-scale autoencoder algorithm for fabric defect detection Type de document : texte imprimé Auteurs : Hongwei Zhang, Auteur ; Yanzi Wu, Auteur ; Shuai Lu, Auteur ; Le Yao, Auteur ; Pengfei Li, Auteur Année de publication : 2024 Article en page(s) : p. 451-466 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Algorithmes Analyse multiéchelle Détection de défauts (Ingénierie) Textiles et tissus -- Défauts Résumé : Aiming at the defects in the process of fabric production, a defect detection model of fabric based on a mixed-attention-based multi-scale non-skipping U-shaped deep convolutional autoencoder (MADCAE) was proposed. In a traditional encoder, the convolutional layer treats each pixel equally, so the importance of different pixels cannot be reflected. It is difficult to obtain richer and more effective information. The reconstruction of the defect region and the detection of the defect region are further affected. In this article, three different scale features of input images are extracted by enlarging the receptive field with large kernel convolution blocks. A hybrid attention module is used to ensure the richness of the extracted information in terms of space and channel. Experiments show that this method can effectively reconstruct fabric parts without requiring a large number of defect marking samples. It can quickly detect and locate defective areas of fabric patterns. Note de contenu : - RELATED WORKS : Fabric defect detection method based on deep learning - Attention mechanism - M-Net - METHOD : Proposed model - Training of MADCAE-based models - Defect detection of yarn-dyed fabric parts based on the MADCAE model - EXPERIMENT : Dataset - Introduction to the experimental platform - Evaluation indicators - Analysis of experimental results - Ablation experiments DOI : https://doi.org/10.1111/cote.12725 En ligne : https://drive.google.com/file/d/13UA30D1lBJDI0x_rBRWtJcaRwHh-eJmg/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=40962 [article]

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