Residual Attention Smoothing Mixup Network for Efficient Oil Country Tubular Goods Defect Classification

Image classification is a fundamental task in computer vision, with deep learning significantly improving its accuracy. However, the accurate classification of defect types in industrial imaging, such as for oil country tubular goods (OCTGs), remains a challenge, particularly when dealing with limited datasets. This paper addresses the classification of four distinct damage types in OCTG images under small sample conditions using the residual attention smoothing mixup network (RASMN) model. Our approach integrates a residual attention network for efficient feature extraction, label smoothing to mitigate overfitting, and mixup data augmentation for enhanced model robustness. Experimental results demonstrate that RASMN significantly improves classification accuracy, achieving a Top‐1 error rate of 7.6%. This represents a substantial improvement, cutting the error of our baseline residual attention network (15.5%) by more than half and outperforming widely‐used architectures like ResNet18 (16.4%) on this specific task. The significance of these results lies in providing a validated, high‐performance model for a challenging industrial classification task with limited data, balancing high accuracy with an efficient inference time of 3.94 ms. This study offers an effective deep learning solution for classifying tube defect images, highlighting the efficacy of combining residual attention networks with regularization strategies.