Few-shot defect recognition for the multi-domain industry via attention embedding and fine-grained feature enhancement

Abstract

Defect recognition is an effective quality control measure for the key manufacturing process nodes of industrial products. However, current defect recognition models, which rely on a large amount of supervised data, cannot quickly adapt and identify new classes of defects that appear in the production process. Moreover, automated industrial defect recognition is plagued by the problems of high expert annotation costs, scarce samples, and a wide variety of defects. To address these issues, this study explored a few-shot defect recognition method for the multidomain industry using attention embedding and fine-grained feature enhancement. To enhance the feature representation of highly discriminative regions in images, a spatial multidirectional group attention convolutional backbone network (Res12_SMGA) was proposed. A multi-directional feature-enhanced representation of the group channel space was achieved by grouping the channels and generating direction-sensitive feature mappings. In addition, a lightweight feature enhancement branch (LFEB) was designed to select more discriminative pixel-level features from the enhanced high-dimensional features, while aiming to improve the generalization ability for multidomain industrial few-shot defect recognition tasks. Subsequently, the different granularity feature outputs of Res12_SMGA and LFEB were fused, and defect recognition was completed using the Nearest Class Mean classifier (NCM). Experiments showed that the proposed method achieved optimal performance in identifying 10 types of defects in five industrial product domains. For the 10way-5shot and 5way-5shot settings, the classification accuracies were 91.71% and 95.97%, respectively. Thus, this study demonstrated that new industrial defects can be recognized using only a few labelled samples.