Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision

Understand

Although existing image anomaly detection methods yield impressive results, they are mostly an offline learning paradigm that requires excessive data pre-collection, limiting their adaptability in industrial scenarios with online streaming data.

• Online learning-based image anomaly detection methods are more compatible with industrial online streaming data but are rarely noticed.
• For the first time, this paper presents a fully online learning image anomaly detection method, namely LeMO, learning memory for online image anomaly detection.
• LeMO leverages learnable memory initialized with orthogonal random noise, eliminating the need for excessive data in memory initialization and circumventing the inefficiencies of offline data collection.