Component-aware anomaly detection framework for adjustable and logical
  industrial visual inspection
Tongkun Liu Bing Li Xiao Du Bingke Jiang Xiao Jin Liuyi Jin Zhuo Zhao
Industrial visual inspection aims at detecting surface defects in products
during the manufacturing process. Although existing anomaly detection models
have shown great performance on many public benchmarks, their limited
adjustability and ability to detect logical anomalies hinder their broader use
in real-world settings. To this end, in this paper, we propose a novel
component-aware anomaly detection framework (ComAD) which can simultaneously
achieve adjustable and logical anomaly detection for industrial scenarios.
Specifically, we propose to segment images into multiple components based on a
lightweight and nearly training-free unsupervised semantic segmentation model.
Then, we design an interpretable logical anomaly detection model through
modeling the metrological features of each component and their relationships.
Despite its simplicity, our framework achieves state-of-the-art performance on
image-level logical anomaly detection. Meanwhile, segmenting a product image
into multiple components provides a novel perspective for industrial visual
inspection, demonstrating great potential in model customization, noise
resistance, and anomaly classification. The code will be available at
https://github.com/liutongkun/ComAD.