title: 3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised
    Anomaly
summary: 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.
paper_authors._id: 67aee1cd7af05a21a72e793d
paper_authors.name: Enquan Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-14T08:01:03.483Z
paper_authors._id: 648bf9afded4c3eb970eca85
paper_authors.avatarUrl: /avatars/a4b7b7fd6c1fca0eac85da7383f58361.svg
paper_authors.fullname: enquan yang
paper_authors.type: user
paper_authors.user: enquan2022
paper_authors._id: 67aee1cd7af05a21a72e793e
paper_authors.name: Peng Xing
paper_authors._id: 67aee1cd7af05a21a72e793f
paper_authors.name: Hanyang Sun
paper_authors._id: 67aee1cd7af05a21a72e7940
paper_authors.name: Wenbo Guo
paper_authors._id: 67aee1cd7af05a21a72e7941
paper_authors.name: Yuanwei Ma
paper_authors._id: 67aee1cd7af05a21a72e7942
paper_authors.name: Zechao Li
paper_authors._id: 67aee1cd7af05a21a72e7943
paper_authors.name: Dan Zeng
paper_title: 3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised
    Anomaly
paper_summary: 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.
paper_ai_keywords: heterogeneous distillation model segmentation model coarse-to-fine detection paradigm recovery features Recovery Guidance (CFRG) unsupervised anomaly detection