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A Deep Learning Approach to Classifying Tyres Using Sidewall Images

In: Resilience, Entrepreneurship and ICT

Author

Listed:
  • Dean Gifford

    (Nelson Mandela University)

  • Jean Greyling

    (Nelson Mandela University)

Abstract

End of Life Tyres (ELTs) 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. Four identifying features of tyres were investigated, namely: the Department of Transport (DOT) code, tread pattern, tyre size and the logo. The tyre logo was identified to be the classifying element for this research as it was the highest position on a derived hierarchy of identifiers for tyres. 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. K-fold cross-validation technique was adopted to ensure that variance was accounted. The results 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).

Suggested Citation

  • Dean Gifford & Jean Greyling, 2021. "A Deep Learning Approach to Classifying Tyres Using Sidewall Images," CSR, Sustainability, Ethics & Governance, in: Jantje Halberstadt & Jorge Marx Gómez & Jean Greyling & Tulimevava Kaunapawa Mufeti & Helmut Faasch (ed.), Resilience, Entrepreneurship and ICT, pages 331-354, Springer.
    • Handle: RePEc:spr:csrchp:978-3-030-78941-1_16
    DOI: 10.1007/978-3-030-78941-1_16
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