Defect classification on limited labeled samples with multiscale feature fusion and semi-supervised learning

Defect inspection is an essential part of ensuring the quality of industrial products. Deep learning has achieved great success in defect inspection when a large number of labeled samples are available. However, it is infeasible to collect and label numerous samples in many manufacturing processes. Meanwhile, deep learning methods cannot conform to the high defect recognition accuracy of strict production requirements when the labeled samples are scarce but varied. This paper proposed a novel convolutional neural network architecture and a semi-supervised learning strategy using soft pseudo labels and a mutual correction classifier to improve the defect inspection accuracy when labeled samples are scarce. The effectiveness of the proposed method is verified on a famous industrial defect inspection benchmark dataset and a practical dataset containing images collected from actual injection molding production lines. The results indicate that the proposed method achieves an accuracy of 99.03% on the benchmark defect dataset, which is approximately 13.2% higher than other methods when the training dataset contains only 45 labeled images and 135 unlabeled samples per category. The best accuracy on the benchmark dataset obtained by the proposed method reaches 99.72%. Besides, an average accuracy of 99.25% is achieved with only 20 labeled samples and 180 unlabeled samples per category in the practical defect inspection task. Visualization methods prove that the performance improvement comes from the proposed multiscale architecture and the semi-supervised learning strategy. The proposed method can be used in practical defect inspection applications of industrial manufacturing, such as steel rolling, welding, and injection molding.