组件感知的工业视觉检测可调与逻辑异常检测框架
Component-aware anomaly detection framework for adjustable and logical industrial visual inspection
AI总结：
提出组件感知异常检测框架ComAD，通过无监督分割与计量特征建模，实现可调且逻辑的工业视觉异常检测，性能达最优。
摘要：
工业视觉检测旨在制造过程中检测产品表面的缺陷。尽管现有的异常检测模型在许多公开基准上表现出色，但其有限的可调性和检测逻辑异常的能力限制了它们在真实场景中的广泛应用。为此，本文提出了一种新颖的组件感知异常检测框架（ComAD），该框架能够同时实现工业场景下的可调和逻辑异常检测。具体而言，我们基于一个轻量级且几乎无需训练的的无监督语义分割模型，将图像分割为多个组件。然后，通过建模每个组件的计量特征及其关系，我们设计了一个可解释的逻辑异常检测模型。尽管该框架简单，但在图像级逻辑异常检测上达到了最先进的性能。同时，将产品图像分割为多个组件为工业视觉检测提供了新的视角，展示了在模型定制、抗噪声和异常分类方面的巨大潜力。代码将在https://github.com/liutongkun/ComAD上提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
