3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised
  Anomaly
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.
67aee1cd7af05a21a72e793d
Enquan Yang
claimed_verified
2025-02-14T08:01:03.483Z
648bf9afded4c3eb970eca85
/avatars/a4b7b7fd6c1fca0eac85da7383f58361.svg
enquan yang
user
enquan2022
67aee1cd7af05a21a72e793e
Peng Xing
67aee1cd7af05a21a72e793f
Hanyang Sun
67aee1cd7af05a21a72e7940
Wenbo Guo
67aee1cd7af05a21a72e7941
Yuanwei Ma
67aee1cd7af05a21a72e7942
Zechao Li
67aee1cd7af05a21a72e7943
Dan Zeng
3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised
  Anomaly
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.
A new 3CAD dataset and a Coarse-to-Fine detection paradigm with Recovery Guidance (CFRG) framework are introduced for unsupervised anomaly detection in industrial settings.
3CAD Coarse-to-Fine detection paradigm Recovery Guidance anomaly detection heterogeneous distillation model segmentation model pixel-level anomalies