ISP-AD：用于推进工业异常检测的大规模真实世界数据集，包含合成缺陷与真实缺陷
ISP-AD: A Large-Scale Real-World Dataset for Advancing Industrial Anomaly Detection with Synthetic and Real Defects
AI总结：
本文提出含合成与真实缺陷的大规模工业数据集ISP-AD，开展混合监督训练实验，发现少量真实缺陷可提升模型泛化，为工业异常检测研究提供基准。
摘要：
采用机器学习的自动视觉检测是实现工业零缺陷政策的关键。异常检测研究受限于数据集的可用性，这类数据集需捕获生产过程中典型的复杂缺陷外观与不理想成像条件。近期基准显示，多数公开数据集偏向最优成像条件，导致其在真实工业场景中的适用性被高估。为解决这一差距，我们推出工业丝网印刷异常检测数据集（Industrial Screen Printing Anomaly Detection Dataset，ISP-AD）。该数据集呈现了嵌入高允许设计变异性结构化图案中的具挑战性的微小、弱对比度表面缺陷。据我们所知，它是迄今为止最大的公开工业数据集，包含直接从工厂车间收集的合成缺陷与真实缺陷。除对近期无监督异常检测方法进行基准测试外，我们还开展了结合合成缺陷与真实缺陷的混合监督训练策略实验。实验表明，即便少量注入的弱标注真实缺陷也能提升泛化能力；此外，从纯合成缺陷训练起步，可将新出现的真实缺陷样本高效整合到后续可扩展训练中。总体而言，我们的发现表明，无模型的合成缺陷可提供冷启动基线，而少量注入的真实缺陷能优化针对此前未见缺陷特征的决策边界。所提出的无监督与监督数据集划分旨在强调对无监督、自监督及监督方法的研究，提升其在工业场景中的适用性。
英文摘要：
Automatic visual inspection using machine learning plays a key role in achieving zero-defect policies in industry. Research on anomaly detection is constrained by the availability of datasets that capture complex defect appearances and imperfect imaging conditions, which are typical of production processes. Recent benchmarks indicate that most publicly available datasets are biased towards optimal imaging conditions, leading to an overestimation of their applicability in 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. Beyond benchmarking recent unsupervised anomaly detection methods, experiments on a mixed supervised training strategy, incorporating both synthesized and real defects, were conducted. Experiments show that even a small amount of injected, weakly labeled real defects improves generalization. Furthermore, starting from training on purely synthetic defects, emerging real defective samples can be efficiently integrated into subsequent scalable training. Overall, our findings indicate that model-free synthetic defects can provide a cold-start baseline, whereas a small number of injected real defects refine the decision boundary for previously unseen defect characteristics. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
