- Title
- A deep learning approach to classifying tyres using sidewall images
- Creator
- Gifford, Dean
- Subject
- Image processing -- Digital techniques
- Subject
- Image processing Computer science
- Date Issued
- 2019
- Date
- 2019
- Type
- Thesis
- Type
- Masters
- Type
- MSc
- Identifier
- http://hdl.handle.net/10948/39720
- Identifier
- vital:35351
- Description
- End of Life Tyres (ELT's) pose a potential health and environmental risk when dumped in illegal stockpiles. For recycling to be considered feasible, a profitable business opportunity needs to be created. One method of making the recycling process of tyres more profitable is by understanding the compounds found within each tyre. This study aims at classifying these tyres in order to achieve this knowledge. A literature review was done to investigate neural networks, convolutional neural networks as well as existing deep learning architectures for image classification. A deep learning approach was applied in order to classify the logos of tyres as these approaches have proved their success in both image classification and more specifically logo classification. Although tyre classification has been implemented in the past, a deep learning approach has not been applied and the logo has not been the classifying element in any other studies. The main difference of this study compared to previous research surrounding deep learning and logo classification is the properties of the tyre logo. Logos on tyres are very similar in colour as they are purely formed in rubber and very seldom have any colour to them. Additionally, the embossed logos can contain variation among same branded tyres due to small inconsistencies in the moulds. The implementation of this deep learning solution saw multiple convolutional neural networks implemented. Some of these architectures were also implemented using transferred learning. The metrics obtained as outputs from training and testing the architectures were the accuracy, precision, recall, and F1-score. These metrics were compared in conjunction with the confusion matrix produced from testing. To ensure that variance was accounted for in the experiments, the k-fold cross-validation technique was adopted. The results of this study identified that one convolutional neural network model, MobileNet, was particularly well suited for the context of classifying logos on tyre sidewalls. The MobileNet architecture had the highest performance metrics for both training from scratch (96.7% accuracy) and transferred learning (98.8% accuracy). Three other models performed particularly well when trained from scratch, these were a modification of the LeNet architecture, ResNet50 and InceptionV3. The transferred learning results were also impressive with four out of the 5 models achieving an accuracy above 94%. Interestingly, the ResNet50 architecture failed to train when transferred learning was applied. Contrasting to this, the two models VGG16 and VGG19 failed to train when trained from scratch but performed equally as well as the other models when transferred learning was implemented. This indicates that although transferred learning can improve the performance of models, it is highly dependent on the task as well as the model. Overall the results obtained proved that a deep learning approach could be applied in order to classify tyres accurately.
- Format
- xi, 155 leaves
- Format
- Publisher
- Nelson Mandela University
- Publisher
- Faculty of Science
- Language
- English
- Rights
- Nelson Mandela University
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Collections
NMU School of Computer Science, Mathematics, Physics and Statistics
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | A Deep Learning Approach to Classifying Tyres using Sidewall.pdf | 14 MB | Adobe Acrobat PDF | View Details Download |