3CAD：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
AI总结：
本文构建源自真实3C生产线的大规模像素级异常检测数据集3CAD，并提出由异构蒸馏、分割和恢复特征引导组成的CFRG无监督检测框架。
摘要：
工业异常检测得益于MVTec-AD和VisA等数据集而取得进展。然而，这些数据集在缺陷样本数量、缺陷类型以及真实场景可用性方面存在局限。这些限制阻碍了研究人员进一步探索更高精度的工业检测性能。为此，我们提出了一个名为3CAD的新型大规模异常检测数据集，该数据集来源于真实的3C生产线。具体而言，所提出的3CAD包含八种不同类型的制造零件，共计27,039张带有像素级异常标注的高分辨率图像。3CAD的关键特征在于，它覆盖了不同尺寸的异常区域、多种异常类型，并且每张异常图像中可能存在多个异常区域和多种异常类型。这是首个且规模最大的、专用于3C产品质量控制的异常检测数据集，可供社区探索和开发。同时，我们提出了一个简单而有效的无监督异常检测框架：带有恢复引导的由粗到细检测范式（CFRG）。为检测小缺陷异常，所提出的CFRG采用由粗到细的检测范式。具体而言，我们利用异构蒸馏模型进行粗定位，随后通过分割模型进行精细定位。此外，为更好地捕捉正常模式，我们引入恢复特征作为引导。最后，我们报告了CFRG框架与流行异常检测方法在3CAD数据集上的结果，展示出强劲竞争力，并提供了一个极具挑战性的基准以推动异常检测领域的发展。数据和代码可在https://github.com/EnquanYang2022/3CAD获取。
英文摘要：
Industrial anomaly detection achieves progress thanks to datasets such as MVTec-AD and VisA. However, they suffer from limitations in terms of the number of defect samples, 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 production 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 detection dataset dedicated to 3C product quality control for community exploration and development. Meanwhile, we introduce a simple yet effective framework for unsupervised anomaly detection: a Coarse-to-Fine detection paradigm with Recovery Guidance (CFRG). To detect small defect anomalies, the proposed CFRG utilizes a coarse-to-fine detection paradigm. Specifically, we utilize a heterogeneous distillation model for coarse localization and then fine localization 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 framework 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.
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。
