3CAD：一个用于无监督异常检测的大规模真实世界3C产品数据集

3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly

February 9, 2025

作者: Enquan Yang, Peng Xing, Hanyang Sun, Wenbo Guo, Yuanwei Ma, Zechao Li, Dan Zeng

cs.AI

摘要

工业异常检测取得了进展，得益于诸如MVTec-AD和VisA之类的数据集。然而，它们在缺陷样本数量、缺陷类型和真实场景可用性方面存在局限性。这些限制阻碍了研究人员进一步探索具有更高准确性的工业检测性能。为此，我们提出了一个新的大规模异常检测数据集，名为3CAD，它源自真实的3C生产线。具体来说，所提出的3CAD包括八种不同类型的制造零件，共计27,039张高分辨率图像，标有像素级异常。3CAD的关键特点是涵盖了不同大小的异常区域、多种异常类型，以及每个异常图像可能存在多个异常区域和多种异常类型。这是首个专门用于3C产品质量控制的最大异常检测数据集，供社区探索和发展使用。同时，我们介绍了一种简单而有效的无监督异常检测框架：一种带有恢复引导的粗到细检测范式（CFRG）。为了检测小缺陷异常，所提出的CFRG利用了粗到细的检测范式。具体来说，我们利用异构蒸馏模型进行粗定位，然后通过分割模型进行精确定位。此外，为了更好地捕捉正常模式，我们引入了恢复特征作为引导。最后，我们在3CAD数据集上报告了我们的CFRG框架和流行的异常检测方法的结果，展示了强大的竞争力，并提供了一个极具挑战性的基准，促进异常检测领域的发展。数据和代码可在以下链接找到：https://github.com/EnquanYang2022/3CAD。

English

Industrial anomaly detection achieves progress thanks to datasets such as MVTec-AD and VisA. However, they suf- fer from limitations in terms of the number of defect sam- ples, types of defects, and availability of real-world scenes. These constraints inhibit researchers from further exploring the performance of industrial detection with higher accuracy. To this end, we propose a new large-scale anomaly detection dataset called 3CAD, which is derived from real 3C produc- tion lines. Specifically, the proposed 3CAD includes eight different types of manufactured parts, totaling 27,039 high- resolution images labeled with pixel-level anomalies. The key features of 3CAD are that it covers anomalous regions of different sizes, multiple anomaly types, and the possibility of multiple anomalous regions and multiple anomaly types per anomaly image. This is the largest and first anomaly de- tection dataset dedicated to 3C product quality control for community exploration and development. Meanwhile, we in- troduce a simple yet effective framework for unsupervised anomaly detection: a Coarse-to-Fine detection paradigm with Recovery Guidance (CFRG). To detect small defect anoma- lies, the proposed CFRG utilizes a coarse-to-fine detection paradigm. Specifically, we utilize a heterogeneous distilla- tion model for coarse localization and then fine localiza- tion through a segmentation model. In addition, to better capture normal patterns, we introduce recovery features as guidance. Finally, we report the results of our CFRG frame- work and popular anomaly detection methods on the 3CAD dataset, demonstrating strong competitiveness and providing a highly challenging benchmark to promote the development of the anomaly detection field. Data and code are available: https://github.com/EnquanYang2022/3CAD.