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

TL;DR

The paper introduces 3CAD, a large-scale real-world dataset for unsupervised anomaly detection in 3C products, along with a novel detection framework called CFRG that improves localization of small defects.

Contribution

It provides the first large-scale 3C product anomaly dataset and proposes a new coarse-to-fine detection framework with recovery guidance for enhanced defect localization.

Findings

3CAD is the largest 3C anomaly dataset with pixel-level labels.

CFRG outperforms existing methods in detecting small defects.

The dataset and framework facilitate further research in industrial anomaly detection.

Abstract

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.