A Multi-level spatial feature fusion-based transformer for intelligent defect recognition with small samples toward smart manufacturing system

Abstract

ABSTRACT Smart is a development trend in manufacturing systems, and intelligent defect recognition is essential in smart manufacturing systems for both quality control and decision-making. But the recognition performance of the current methods still needs to be improved, as well as the interpretability. As a hotspot, Transformer (ViT) has outstanding performance and interpretability on image recognition, which has shown the potential for intelligent defect recognition. However, ViT requires large numbers of samples, while small-sample is common in real-world cases, which contain less information, and this will cause ViT overfitting and misclassifying. Thus, it impedes the application of ViT greatly. To address this problem, a multi-scale spatial feature fusion-based ViT is proposed for small-sample defect recognition. The proposed method simulates human vision to extract the multi-level features of defects, and three improved ViTs are built to fuse the features. The experimental results indicate that the proposed method achieves improved performance on small-sample defect recognition. Compared with the DL and defect recognition methods, the accuracies are improved by 1.5%~20.07% on wood defects, and achieve an accuracy of 100% on steel defects. Furthermore, the visualization results also show that the proposed method is explicable, and it is helpful for defect analysis.