Defect Detection in Tire X-Ray Images: Conventional Methods Meet Deep Structures
It addresses the problem of automated quality control in tire manufacturing, but the approach is incremental as it combines existing methods rather than introducing a fundamentally new technique.
This paper tackles automated defect detection in tire X-ray images by integrating traditional feature extraction methods like LBP and GLCM with machine learning models such as Random Forest and YOLOv8, resulting in improved accuracy and reliability for quality assurance in tire manufacturing.
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.