Automatic fabric defect detection using a deep convolutional neural network / Jun-Feng Jing in COLORATION TECHNOLOGY, Vol. 135, N° 3 (06/2019)  

[article]  inCOLORATION TECHNOLOGY > Vol. 135, N° 3 (06/2019) . - p. 213-223 Titre : Automatic fabric defect detection using a deep convolutional neural network Type de document : texte imprimé Auteurs : Jun-Feng Jing, Auteur ; Hao Ma, Auteur ; Huan-Huan Zhang, Auteur Année de publication : 2019 Article en page(s) : p. 213-223 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Détection de défauts (Ingénierie) Réseaux neuronaux (informatique) Textiles et tissus -- Défauts Index. décimale : 667.3 Teinture et impression des tissus Résumé : Fabric defect detection plays an important role in the textile production process, but there are still some challenges in detecting defects rapidly and accurately. In this paper, we propose a powerful detection method for automatic fabric defect detection using a deep convolutional neural network (CNN). It consists of three main steps. First, the fabric image is decomposed into local patches and each local patch is labelled. Then the labelled patches are transmitted to the pretrained deep CNN for transfer learning. Finally, defects are detected during the inspection phase by sliding over the whole image using the trained model, and the category and position of each defect is obtained. The proposed method is validated on two public and one self‐made fabric database. The experimental results demonstrate that our method significantly outperforms selected state‐of‐the art methods in terms of both quality and robustness. Note de contenu : - REVIEW OF PREVIOUS WORK - PTIP METHOD : Automatically calculating the patch size - Manual labelling category - Convolutional neural network - Network model - DATASET : TLDS database - Dark red fabric - Patterned texture fabric (regular patterned fabric - EXPERIMENTAL RESULTS : Accuracy of the TILDA database - Accuracy of dark red fabric - Patterned fabric database - Evaluation of accuracy - Parameter selection DOI : 10.1111/cote.12394 En ligne : https://onlinelibrary.wiley.com/doi/epdf/10.1111/cote.12394 Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=32613 [article]

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Fabric defect detection based on golden image subtraction / Jun-Feng Jing in COLORATION TECHNOLOGY, Vol. 133, N° 1 (02/2017)  

[article]  inCOLORATION TECHNOLOGY > Vol. 133, N° 1 (02/2017) . - p. 26–39 Titre : Fabric defect detection based on golden image subtraction Type de document : texte imprimé Auteurs : Jun-Feng Jing, Auteur ; Shan Chen, Auteur ; Peng-Fei Li, Auteur Année de publication : 2017 Article en page(s) : p. 26–39 Note générale : Bibliogr. Langues : Anglais (eng) Catégories : Gabor, Filtre de Un filtre de Gabor est un filtre linéaire dont la réponse impulsionnelle est une sinusoïde modulée par une fonction gaussienne (également appelée ondelette de Gabor). Il porte le nom du physicien anglais d'origine hongroise Dennis Gabor. Imagerie (technique) Textiles et tissus -- Défauts Index. décimale : 667.3 Teinture et impression des tissus Résumé : To realise the universality and practicality of fabric defect detection in the textile industry, this paper proposes two approaches based on the Gabor filter and the golden image subtraction method. A method known as Gabor preprocessed golden image subtraction is first introduced, which filters a test fabric image by the real component of the Gabor filter with a 1 Hz centre frequency and a 90° angle. Golden image subtraction performs subtractions between the golden template and the filtered image to obtain a resultant image, and the segmentation threshold is determined by the direct threshold. The second method is Gabor preprocessed golden image subtraction based on a genetic algorithm, which can automatically select the parameter groups of the Gabor filter via the genetic algorithm. In addition, the paper also presents an extensive comparison between the proposed methods and wavelet preprocessed golden image subtraction. Meanwhile, the performances of the aforementioned three methods are tested in a real machine vision detection system to meet the actual demands of the textile industry. It can be concluded that Gabor preprocessed golden image subtraction provides the best detection results. The overall detection success rate is 95.62%, with 80 defect-free images and 80 defective images for fabric textures of common types. Note de contenu : - EXPERIMENTAL : Image processing theories - GIS based on Gabor filter - Parameter tuning - RESULTS AND DISCUSSION : Machine vision system - Experimental materials - Results of GGIS and GAGIS - Comparison with WGIS DOI : 10.1111/cote.12239 En ligne : https://drive.google.com/file/d/1HyYoUkencCMWErVOxOZXXPmJ97lnuzbD/view?usp=drive [...] Format de la ressource électronique : Pdf Permalink : https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=27877 [article]

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