Component-aware anomaly detection framework for adjustable and logical industrial visual inspection
This addresses the need for more adaptable and logical anomaly detection in real-world industrial manufacturing settings, representing a domain-specific incremental improvement.
The paper tackles the problem of limited adjustability and logical anomaly detection in industrial visual inspection by proposing a component-aware anomaly detection framework (ComAD), which achieves state-of-the-art performance on image-level logical anomaly detection.
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.