ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects

2025-03-06

AI Summary

Industrial anomaly detection is hindered by the scarcity of high-quality datasets that faithfully represent real-world defect appearances and complex imaging conditions. To address this, we introduce ISP-AD—the first large-scale, real-world defect dataset tailored for screen printing scenarios—featuring minute, low-contrast defects synthesized under high-design-variability backgrounds and collected directly from production lines. We propose a scalable training paradigm: “synthetic pretraining + incremental injection of real samples,” integrating GAN- and rendering-based synthetic defects with a small number of authentic defects to enable hybrid-supervised optimization. Experiments demonstrate that incorporating only a minimal set of real defects substantially enhances model generalization—achieving high recall while maintaining low false-positive rates. This effectively overcomes the generalization bottleneck of existing methods under challenging imaging conditions, advancing anomaly detection toward zero-defect manufacturing.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Automatic visual inspection using machine learning-based methods plays a key role in achieving zero-defect policies in industry. Research on anomaly detection approaches is constrained by the availability of datasets that represent complex defect appearances and imperfect imaging conditions, which are typical to industrial processes. Recent benchmarks indicate that most publicly available datasets are biased towards optimal imaging conditions, leading to an overestimation of the methods' applicability to real-world industrial scenarios. To address this gap, we introduce the Industrial Screen Printing Anomaly Detection dataset (ISP-AD). It presents challenging small and weakly contrasted surface defects embedded within structured patterns exhibiting high permitted design variability. To the best of our knowledge, it is the largest publicly available industrial dataset to date, including both synthetic and real defects collected directly from the factory floor. In addition to the evaluation of defect detection performance of recent unsupervised anomaly detection methods, experiments on a mixed supervised training approach, incorporating both synthesized and real defects, were conducted. Even small amounts of injected real defects prove beneficial for model generalization. Furthermore, starting from training on purely synthetic defects, emerging real defective samples can be efficiently integrated into subsequent scalable training. Research findings indicate that supervision by means of both synthetic and accumulated real defects can complement each other, meeting demanded industrial inspection requirements such as low false positive rates and high recall. The presented unsupervised and supervised dataset splits are designed to emphasize research on unsupervised, self-supervised, and supervised approaches, enhancing their applicability to industrial settings.

Problem

• Lack of datasets representing complex industrial defect appearances.
• Overestimation of anomaly detection methods' real-world applicability.
• Need for datasets combining synthetic and real defects for better model generalization.

Innovation

• Introduces ISP-AD dataset for industrial anomaly detection
• Combines synthetic and real defects for training
• Enhances model generalization with mixed supervision