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Industrial visual inspection aims at detecting surface defects in productsduring the manufacturing process. Although existing anomaly detection modelshave shown great performance on many public benchmarks, their limitedadjustability and ability to detect logical anomalies hinder their broader usein real-world settings. To this end, in this paper, we propose a novelcomponent-aware anomaly detection framework (ComAD) which can simultaneouslyachieve adjustable and logical anomaly detection for industrial scenarios.Specifically, we propose to segment images into multiple components based on alightweight and nearly training-free unsupervised semantic segmentation model.Then, we design an interpretable logical anomaly detection model throughmodeling the metrological features of each component and their relationships.Despite its simplicity, our framework achieves state-of-the-art performance onimage-level logical anomaly detection. Meanwhile, segmenting a product imageinto multiple components provides a novel perspective for industrial visualinspection, demonstrating great potential in model customization, noiseresistance, and anomaly classification. The code will be available athttps://github.com/liutongkun/ComAD.

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