Prototype-Based Supervised Contrastive Learning Method for Noisy Label Correction in Tire Defect Detection

The defect detection of industrial products is an essential task in industrial production. In the field of tire defect detection, X-ray imaging sensors are employed by tire factories to investigate the inner structure of the tires and identify defects. In recent years, deep learning has become a good alternative to manual inspection, but one major challenge of utilizing deep learning models is the requirement of large datasets with precisely annotated labels. In industrial applications, the process of annotating labels is often dependent on domain experts, which introduces inter- and intra-observer variability. Consequently, industrial datasets often contain noisy labels, which will deteriorate the performance of convolutional neural networks (CNNs) during the training process. In this study, we propose an approach called prototype-based supervised contrastive learning (PSCL) to address label noise problems. First, we utilize a supervised contrastive learning (SCL) framework to pull together features of samples belonging to the same class and produce class prototypes based on these features. Subsequently, label correction is performed using the updated class prototypes. In return, these corrected labels will contribute to the training of a reliable network, which ensures the provision of better prototypes. To assess the effectiveness of our method, we conduct extensive experiments on both synthetic noisy datasets and industrial tire datasets. The results demonstrate the robustness of our approach even in datasets with a large proportion of noisy labels. Therefore, our approach provides a promising solution for mitigating label noise problems in industrial datasets, offering great potential for practical industrial applications.