  This paper introduces a robust approach for automated defect detection in
tire X-ray images by harnessing traditional feature extraction methods such as
Local Binary Pattern (LBP) and Gray Level Co-Occurrence Matrix (GLCM) features,
as well as Fourier and Wavelet-based features, complemented by advanced machine
learning techniques. Recognizing the challenges inherent in the complex
patterns and textures of tire X-ray images, the study emphasizes the
significance of feature engineering to enhance the performance of defect
detection systems. By meticulously integrating combinations of these features
with a Random Forest (RF) classifier and comparing them against advanced models
like YOLOv8, the research not only benchmarks the performance of traditional
features in defect detection but also explores the synergy between classical
and modern approaches. The experimental results demonstrate that these
traditional features, when fine-tuned and combined with machine learning
models, can significantly improve the accuracy and reliability of tire defect
detection, aiming to set a new standard in automated quality assurance in tire
manufacturing.
