Akademik Veri Yönetim Sistemi
Engineering Research Express, cilt.6, sa.45423, ss.1-16, 2024 (ESCI)
Safety of drivers and passengers is an important quality assurance aim of the tire manufacturers. Tires that have air bubbles in them are a frequent cause of accidents. Recently, manufacturers have placed a high value on employing laborers with tire diagnostic experience. Along with the expenditure of time and money, this process also necessitates a sizable workforce of experienced laborers. The detection of tire defects can be automated utilizing a number of different ways, greatly reducing the margin for human error. One such method is digital shearography, which can be used to detect air bubbles in images. In this study, transfer learning based models are proposed to classify air bubble defects in tire shearography images. Proposed method employed in transfer learning with several deep learning CNN-based models, including VGG16, VGG19, ResNet50, Xception, MobileNetV2 and DenseNet201. Owing to the assistance of the experts at the Pirelli Automobile Tires Izmit factory, we were able to collect and label the dataset used in this study. The dataset contains 1392 tire shearography images. 811 of these images belong to the 'good' class, while the remaining 581 images belong to the 'bad' class. The proposed method in this study achieves remarkable bubble detection results over 95% in terms of accuracy and other metrics with the DenseNet201 model. The results indicate that the proposed model has comparable accuracy and recall with the existing studies. This study is valuable for the tire industry, as the existing literature offers limited research on the classification of tire bubble defects, the differentiation between defective and defect-free tires, and the categorization of various defect types. In addition, the proposed model offers several advantages over existing studies, including higher robustness against overfitting, suitability for real-time industrial applications, and improved efficiency in processing, making a practical solution for defect detection in industrial settings.