Cross-domain few-shot defect recognition for metal surfaces. (1st January 2023)

• Record Type: Journal Article
• Authors: Duan, Guifang; Song, Yiguo; Liu, Zhenyu; Ling, Shiquan; Tan, Jianrong
• Is Part Of: Measurement science & technology. Volume 34:Number 1(2023)
• Publication Date: 2023-01-01
• Subjects: few-shot recognition -- defect recognition -- metal surface; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac90de
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Defect recognition for metal surfaces in the industry has attracted more and more attention. However, defect data scarcity presents a huge challenge for defect recognition in real industrial scenarios. The traditional few-shot defect recognition method can address this problem when the training data and test data are collected from the same or a similar metal surface. However, the defect data from similar metal surfaces are difficult to acquire to a certain extent. In this paper, we introduce a novel task setting that can achieve few-shot defect recognition by transferring knowledge across domains. The method consists of two levels: image-level and feature-level. At the image-level, a meta-augmentation method is proposed to improve the recognition generalization in each meta-task by joint parameter updating from the original and augmented domains. At the feature-level, a class covariance-guided feature perturbation method is proposed to perturb the feature distribution to enhance the cross-domain generalization capability. The extension of cross-domain experiments from textured to metal surfaces shows the superior performance of the proposed method compared to other mainstream methods.