| 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 |
in COLORATION TECHNOLOGY > Vol. 135, N° 3 (06/2019) . - p. 213-223
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