A novel feature-based network with sequential information for textile defect detection / Li Chenxi in COLORATION TECHNOLOGY, Vol. 136, N° 6 (12/2020)  

[article]  inCOLORATION TECHNOLOGY > Vol. 136, N° 6 (12/2020) . - p. 476-484 Titre : A novel feature-based network with sequential information for textile defect detection Type de document : texte imprimé Auteurs : Li Chenxi, Auteur ; Liu Tao, Auteur ; We Ye, Auteur Année de publication : 2020 Article en page(s) : p. 476-484 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Détection de défauts (Ingénierie) Qualité -- Contrôle Textiles et tissus -- Défauts Index. décimale : 677 Textiles Résumé : In this paper, a novel feature-based attention network with sequential information is proposed for fabric defect detection. As an important part of the textiles industry, fabric quality inspection needs to be automated in the latest wave of intelligent transformation. To this end, fabric defect detection algorithms have been widely studied. In this paper, an efficient defect detection model for fabrics was built. As the appearances of defects change with the type of fabric, manual features which denote the overall situation of the fabric are used as prior knowledge. The feature-based attention module discussed in this paper can generate attention maps from these manual features to rectify the responses of the feature maps according to the whole situation of the input image. Multidirectional long short-term memory networks are implemented to extract context information from continuous defects. When making a judgement, taking sequential information into consideration may reduce the number of unexpected misjudged decisions compared with the number of those made independently pixel by pixel. Both of those two modules can be integrated into any existing convolutional neural network model and trained in an end-to-end manner. A fabric defect dataset is built to train and test the models. In this paper, several models with different architectures are implemented to verify our ideas, and are supported by results confirming the efficiency of the proposed methods. Note de contenu : - THEORY - PROPOSED METHODS : Feature-based attention module - Multidirectional LSTM module - EXPERIMENTAL : Training and testing details - Fig. 1 : Distribution of defects - Fig. 2 : Defects - Fig. 3 : Network structure - Fig. 4 : Feature-based attention module - Fig. 5 : The main part of the long short-term memory (LSTM) branch - Fig. 6 : Defect - Fig. 7 : Mask results - Table 1 : Evaluation metrics - Table 2 : AUC values within the defect categories DOI : https://doi.org/10.1111/cote.12493 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12493 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=34701 [article]

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