F2PAD: A General Optimization Framework for Feature-Level to Pixel-Level Anomaly Detection

Abstract

Image-based inspection systems have been widely deployed in manufacturing production lines. Due to the scarcity of defective samples, unsupervised anomaly detection that only leverages normal samples during training to detect various defects is popular. Existing feature-based methods, utilizing deep features from pretrained neural networks, show their impressive performance in anomaly localization and the low demand for the sample size for training. However, the detected anomalous regions of these methods always exhibit inaccurate boundaries, which impedes the downstream tasks. This deficiency is caused: (i) The decreased resolution of high-level features compared with the original image, and (ii) The mixture of adjacent normal and anomalous pixels during feature extraction. To address them, we propose a novel unified optimization framework (F2PAD) that leverages the Feature-level information to guide the optimization process for Pixel-level Anomaly Detection in the inference stage. The proposed framework is universal and plug-and-play, which can enhance various feature-based methods with limited assumptions. Case studies are provided to demonstrate the effectiveness of our strategy, particularly when applied to three popular backbone methods: PaDiM, CFLOW-AD, and PatchCore.

Juan Du

Assistant Professor

My research interests include knowledge-infused data science for quality improvement, industrial data analytics and machine learning, and system informatics and control for manufacturing applications.