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.

翻译：本文提出了一种鲁棒的自动化轮胎X射线图像缺陷检测方法，通过融合局部二值模式（LBP）和灰度共生矩阵（GLCM）等传统特征提取方法，以及基于傅里叶变换和小波变换的特征，并辅以先进的机器学习技术。针对轮胎X射线图像中复杂图案和纹理固有的挑战，本研究强调了特征工程对提升缺陷检测系统性能的重要性。通过将这些特征组合与随机森林（RF）分类器进行精细集成，并将其与YOLOv8等先进模型进行对比，本研究不仅对传统特征在缺陷检测中的性能进行了基准测试，还探索了经典方法与现代方法之间的协同作用。实验结果表明，这些传统特征在经过微调并与机器学习模型结合后，能够显著提升轮胎缺陷检测的准确性和可靠性，旨在为轮胎制造的自动化质量保证设立新标准。