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.

翻译：工业视觉检测旨在检测产品制造过程中的表面缺陷。尽管现有异常检测模型在诸多公开基准测试中展现出卓越性能，但其有限的可调节性与检测逻辑异常的能力制约了其在真实场景中的广泛应用。为此，本文提出一种新颖的组件感知异常检测框架（ComAD），该框架能同时实现面向工业场景的可调节与逻辑异常检测。具体而言，我们基于轻量级且近乎免训练的无监督语义分割模型，将图像分割为多个组件。随后，通过建模各组件的计量特征及其相互关系，设计了一种可解释的逻辑异常检测模型。尽管方法简洁，本框架在图像级逻辑异常检测中达到了最优性能。同时，将产品图像分割为多组件为工业视觉检测提供了全新视角，在模型定制、噪声鲁棒性与异常分类方面展现出巨大潜力。相关代码将发布至https://github.com/liutongkun/ComAD。