| Titre : |
An ensemble of fine-tuned deep learning networks for wet-blue leather segmentation |
| Type de document : |
texte imprimé |
| Auteurs : |
Masood Aslam, Auteur ; Tariq M. Khan, Auteur ; Syed Saud Naqvi, Auteur ; Geoff Holmes, Auteur |
| Année de publication : |
2022 |
| Article en page(s) : |
p. 164-170 |
| Langues : |
Américain (ame) |
| Catégories : |
Automatisation
Evaluation visuelle
Qualité -- Contrôle
Surfaces -- Analyse
Wet-blue (tannage)Peau tannée au chrome (le chrome donne une couleur bleue)
|
| Index. décimale : |
675.2 Préparation du cuir naturel. Tannage |
| Résumé : |
As part of industrial quality control in the leather industry, it is important to segment features/defects in wet-blue leather samples. Manual inspection of leather samples is the current norm in industrial settings. To comply with the current industrial standards that advocate large-scale automation, visual inspection based leather processing is imperative. Visual inspection of wet-blue leather features is a challenging problem as the characteristics of these features can take on a variety of shapes and colour variations to constitute various normal and abnormal surface regions. The aim of this work is to automatically segment leather images to detect various features/defects along with the background through visual analysis of the surfaces. To accomplish this, a deep learning-based technique is developed that learns to segment wet-blue leather surface features. On our own curated leather images dataset, the proposed ensemble network performed well, with an F1-Score of 74 percent. |
| Note de contenu : |
- LITERATURE REVIEW
- THE PROPOSED METHOD : Data augmentation - Architectures - Ensembling convolutional neural networks
- EXPERIMENTAL DESIGN : Dataset - Feature/defect types - Ground truth labelling - Experiment configuration - Quantitative measures
- RESULTS : Quantitative comparison - Qualitative comparison
- Table 1 : Comparison of the segmentation performance of our proposed method with other state-of-the-art segmentation odels for the wet-blue leather dataset
- Table 2 : Class-wise comparison of the segmentation performance of state-of-the-art segmentation methods |
| DOI : |
https://doi.org/10.34314/jalca.v117i4.4900 |
| En ligne : |
https://drive.google.com/file/d/1eeGhr_Y_PMJEhoAwWdQfF7edVAP5PF7M/view?usp=drive [...] |
| Format de la ressource électronique : |
Pdf |
| Permalink : |
https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=37563 |
in JOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION (JALCA) > Vol. CXVII, N° 4 (04/2022) . - p. 164-170
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An ensemble of fine-tuned deep learning networks for wet-blue leather segmentation in JOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION (JALCA), Vol. CXVII, N° 4 (04/2022)
