Component-aware anomaly detection framework for adjustable and logical industrial visual inspection

Abstract

Existing industrial visual anomaly detection models can be broadly categorized into two paradigms, i.e., the paradigm based on image reconstruction and the paradigm based on implicit feature modeling. While both of them have demonstrated great performance in detecting low-level structural anomalies, their limited adjustability and ability to detect high-level logical anomalies hinder their broader use in industrial visual inspection. To solve the above issues, this paper proposes a novel paradigm, that is, Component-aware Anomaly Detection (ComAD), which can simultaneously achieve adjustable and logical anomaly detection. Specifically, it segments the original image into diverse components, enabling model adjustability based on components’ significance and long-range modeling capabilities based on components’ metrological characteristics. We demonstrate that the proposed paradigm can be realized by a lightweight and nearly training-free framework. ComAD is evaluated on real-world scenarios to show its adjustability and logical anomaly detection capabilities. It achieves state-of-the-art performance on the challenging MVTec LOCO AD and CAD-SD benchmarks. Code is available at: https://github.com/liutongkun/ComAD.