Colour space conversion model from CMYK to CIELab based on CS-WNN / Zebin Su in COLORATION TECHNOLOGY, Vol. 137, N° 3 (06/2021)  

[article]  inCOLORATION TECHNOLOGY > Vol. 137, N° 3 (06/2021) . - p. 272-279 Titre : Colour space conversion model from CMYK to CIELab based on CS-WNN Type de document : texte imprimé Auteurs : Zebin Su, Auteur ; Jinkai Yang, Auteur ; Pengfei Li, Auteur ; HuanHuan Zhang, Auteur ; Junfeng Jing, Auteur Année de publication : 2021 Article en page(s) : p. 272-279 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Colorimétrie Couleur -- Analyse Index. décimale : 535.6 Couleur Résumé : In the process of colour space conversion from CMYK to CIELab, colour difference will be caused, which has a negative impact on the quality of digital printing products. In this article, an improved wavelet neural network (WNN) model optimised by cuckoo search (CS) algorithm is proposed to reduce the colour difference. Initially, the colour space conversion model based on WNN is established. The CS algorithm is used to optimise the initial weights and parameters of dilation and translation in the WNN model. Then, 1296 samples are made to train the CS-WNN model. Finally, 100 test samples are input into the trained network to obtain the corresponding L, a and b values of CIELab. The experimental results show that the average conversion colour difference (urn:x-wiley:14723581:media:cote12529:cote12529-math-0001) of the proposed model is 3.469. The conversion accuracy and stability of the proposed model are better than the traditional neural network. Note de contenu : - MODEL CONSTRUCTION : Wavelet neural network - Cuckoo search algorithm - Establishment of colour space conversion model based on CS-WNN - EXPERIMENT RESULT - Table 1 : CIELab colour difference of four colour converson models - Table 2 : Independent sample test DOI : https://doi.org/10.1111/cote.12529 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12529 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=36093 [article]

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Knowledge distillation for unsupervised defect detection of yarn-dyed fabric using the system DAERD : Dual attention embedded reconstruction distillation / Hongwei Zhang in COLORATION TECHNOLOGY, Vol. 140, N° 1 (02/2024)  

[article]  inCOLORATION TECHNOLOGY > Vol. 140, N° 1 (02/2024) . - p. 125-143 Titre : Knowledge distillation for unsupervised defect detection of yarn-dyed fabric using the system DAERD : Dual attention embedded reconstruction distillation Type de document : texte imprimé Auteurs : Hongwei Zhang, Auteur ; Shuaibo Liu, Auteur ; Shuai Lu, Auteur ; Le Yao, Auteur ; Pengfei Li, Auteur Année de publication : 2024 Article en page(s) : p. 125-143 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Détection de défauts (Ingénierie) Textiles et tissus teints Index. décimale : 667.3 Teinture et impression des tissus Résumé : Detecting defects of yarn-dyed fabrics automatically in industrial scenarios can improve economic efficiency, but the scarcity of defect samples makes the task more challenging in the customised and small-batch production scenario. At present, most reconstruction-based methods have high requirements on the effect of reconstructing the defect area into the normal area, and the reconstruction performance often determines the final defect detection result. To solve this problem, this article proposes an unsupervised learning framework of dual attention embedded reconstruction distillation. We try to use this novel distillation scheme to provide some contribution to the defect detection field. Firstly, different from the encoder-encoder structure of traditional distillation, the teacher-student network in this article adopts the encoder-decoder structure. The purpose of the student network is to restore the normal feature representation of the pre-trained teacher network. Secondly, this article proposes a dual attention residual module, which can effectively remove redundant information and defective feature information from the teacher network through the double feature weight allocation mechanism. This helps the student network to recover the normal feature information output by the teacher network. Finally, the multi-level training deployment at the feature level in this article aims to make the model obtain accurate defect detection results. The proposed method has been extensively tested on the published fabric dataset YDFID-1, ZJU-Leaper dataset and the anomaly detection dataset MVTec. The results show that this method not only has good performance in fabric defect detection and location but also has universal applicability. Note de contenu : - RELATED WORKS : Fabric defect detection - Attention mechanism - METHODS : The proposed DAERD model - DAERD-based yarn-dyed fabric defect detection - EXPERIMENT : Experimental details - Datasets - Evaluation metric - Experimental results and comparative analysis - Table 1 : Distribution of the number of defective and defect-free samples in the partial dataset of YDFID-1 - Table 2 : Distribution of the number of anomaly-free and anomalous samples in the texture class dataset - Table 3 : Image-level AUROC (%) of defect detection methods on the YDFID-1 dataset - Table 4 : Pixel-level AUROC (%) and AUPRO (%) of defect localisation methods on the YDFID-1 dataset - Table 5 : Image-level AUROC (%) of defect detection methods on the MVTec dataset - Table 6 : Pixel-level AUROC (%) and AUPRO (%) of defect localisation methods on the MVTec dataset - Table 7 : Image-level AUROC (%) of defect detection methods on the ZJU-Leaper dataset - Table 8 : Pixel-level AUROC (%) and AUPRO (%) of defect localisation methods on the ZJU-Leaper dataset - Table 9 : Ablation study of the effect of SimAM (simple, parameter-free, attention module) and SE (squeeze-and-excitation) network on the model performance DOI : https://doi.org/10.1111/cote.12705 En ligne : https://drive.google.com/file/d/1fzssAGVLxuRnKx8FtikkhnIF1yv1cue3/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=40396 [article]

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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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