3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised
  Anomaly
Authors: name: Enquan Yang, name: Peng Xing, name: Hanyang Sun, name: Wenbo Guo, name: Yuanwei Ma, name: Zechao Li, name: Dan Zeng

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.