| Titre : |
Learning to recognize irregular feature on leather surfaces |
| Type de document : |
texte imprimé |
| Auteurs : |
Masood Aslam, Auteur ; Tariq M. Khan, Auteur ; Syed Saud Naqvi, Auteur ; Geoff Holmes, Auteur ; Rafea Naffa, Auteur |
| Année de publication : |
2021 |
| Article en page(s) : |
p. 169-178 |
| Note générale : |
Bibliogr. |
| Langues : |
Américain (ame) |
| Catégories : |
Cuir
Evaluation visuelle
Qualité -- Contrôle
Surfaces -- Analyse
Wet-blue (tannage)Peau tannée au chrome (le chrome donne une couleur bleue)
|
| Index. décimale : |
675 Technologie du cuir et de la fourrure |
| Résumé : |
As part of industrial quality control in the leather industry, it is important to identify the abnormal features 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 irregular surfaces is a challenging problem as the characteristics of the abnormalities can take a variety of shape and color variations. The aim of this work is to automatically categorize leather images into normal or abnormal by visual analysis of the surfaces. To achieve this aim, a deep learning based approach is devised that learns to recognize regular and irregular leather surfaces and categorize leather images on its basis. To this end, we propose an ensemble of multiple convolutional neural networks for classifying leather images. The proposed ensemble network exhibited competitive performance obtaining 92.68% test accuracy on our own curated leather images dataset. |
| Note de contenu : |
- LITERATURE REVIEW
- THE PROPOSED METHOD : Data augmentation - Convolutional neural network ensembles - Setting up the CNN and training process - Global average pooling
- EXPERIMENTAL DESIGN : Dataset - Performance measures - Benchmark deep learning methods - State-of-the-art methods for comparison
- RESULTS : Comparison with descriptors based machine learning methods - Comparison with deep learning based methods - Class Activations Maps (CAM)
- Table 1 : Comparison of models parameters with and without the GAP layer
- Table 2 : Computational complexity of pooling strategies
- Table 3 : Hyper-parameter values used in algorithms
- Table 4 : Comparison with the state-of-the-art methods in terms of classification accuracy. T acc stands for training accuracy, val acc for validation accuracy and test acc for test accuracy
- Table 5 : Comparison of methods in terms of precision, recall, F1-score and AUC
- Table 6 : Comparison of models in terms of accuracy, transfer learning, batch normalization, batch size using global average pooling and image size of 500 x 375 |
| DOI : |
https://doi.org/10.34314/jalca.v116i5.4291 |
| En ligne : |
https://drive.google.com/file/d/1Ubev3KPms4yofj6BFf-ZgdPRP_AoZado/view?usp=drive [...] |
| Format de la ressource électronique : |
Pdf |
| Permalink : |
https://e-campus.itech.fr/pmb/opac_css/index.php?lvl=notice_display&id=35760 |
in JOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION (JALCA) > Vol. CXVI, N° 5 (05/2021) . - p. 169-178
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Learning to recognize irregular feature on leather surfaces in JOURNAL OF THE AMERICAN LEATHER CHEMISTS ASSOCIATION (JALCA), Vol. CXVI, N° 5 (05/2021)
