Industrial Anomaly Detection System: A Multicase Algorithm Leveraging Feature Information and Memory Bank

• Published in: IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 9
• Main Authors: Shi, Yunhan, Gao, Bin, Yang, Geng, Li, Haoran, Lok Woo, Wai
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Algorithms; Anomalies; Anomaly detection; Anomaly detection (AD); Clustering algorithms; defect localization and segmentation; Extraterrestrial measurements; Feature extraction; Input variables; Learning; Measurement; metric learning; Modules; multicase generalization; nondestructive testing; Overfitting; Testing; Training; Vectors

The conventional anomaly detection (AD) methods typically rely on training normal samples without defects to identify deviations from the background. However, these methods suffer from issues, such as missing detection or false detection. Although subsequent research has attempted to improve algorit...