Journal of Platform Technology

ICT Platform Society (ICT플랫폼학회)
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Transfer Learning Models for Enhanced Prediction of Cracked Tires

  • Candra Zonyfar (Sun Moon University, Dept. of Computer Science and Engineering ) ;
  • Taek Lee (Sun Moon University, Dept. of Computer Science and Engineering) ;
  • Jung-Been Lee (Sun Moon University, Dept. of Computer Science and Engineering) ;
  • Jeong-Dong Kim (Sun Moon University, Dept. of Computer Science and Engineering, Genom-Based BioIT Convergence Institute)
  • Received : 2023.09.22
  • Accepted : 2023.12.15
  • Published : 2023.12.30
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Abstract

Regularly inspecting vehicle tires' condition is imperative for driving safety and comfort. Poorly maintained tires can pose fatal risks, leading to accidents. Unfortunately, manual tire visual inspections are often considered no less laborious than employing an automatic tire inspection system. Nevertheless, an automated tire inspection method can significantly enhance driver compliance and awareness, encouraging routine checks. Therefore, there is an urgency for automated tire inspection solutions. Here, we focus on developing a deep learning (DL) model to predict cracked tires. The main idea of this study is to demonstrate the comparative analysis of DenseNet121, VGG-19 and EfficientNet Convolution Neural Network-based (CNN) Transfer Learning (TL) and suggest which model is more recommended for cracked tire classification tasks. To measure the model's effectiveness, we experimented using a publicly accessible dataset of 1028 images categorized into two classes. Our experimental results obtain good performance in terms of accuracy, with 0.9515. This shows that the model is reliable even though it works on a dataset of tire images which are characterized by homogeneous color intensity.

Keywords

References

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