FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection
This work addresses the need for real-time anomaly detection in industrial applications, representing an incremental improvement in speed.
The paper tackled the problem of slow inference speeds in feature embedding-based industrial anomaly detection by proposing Fast Adaptive Patch Memory (FAPM), which achieved real-time performance with competitive accuracy compared to state-of-the-art methods.
Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images. However, some methods do not meet the speed requirements of real-time inference, which is crucial for real-world applications. To address this issue, we propose a new method called Fast Adaptive Patch Memory (FAPM) for real-time industrial anomaly detection. FAPM utilizes patch-wise and layer-wise memory banks that store the embedding features of images at the patch and layer level, respectively, which eliminates unnecessary repetitive computations. We also propose patch-wise adaptive coreset sampling for faster and more accurate detection. FAPM performs well in both accuracy and speed compared to other state-of-the-art methods