Being able to spot defective parts is a critical component in large-scale industrial manufacturing. A particular challenge that we address in this work is the cold-start problem: fit a model using nominal (non-defective) example images only. While handcrafted solutions per class are possible, the goal is to build systems that work well simultaneously on many different tasks automatically. The best performing approaches combine embeddings from ImageNet models with an outlier detection model. In this paper, we extend on this line of work and propose \textbf{PatchCore}, which uses a maximally representative memory bank of nominal patch-features. PatchCore offers competitive inference times while achieving state-of-the-art performance for both detection and localization. On the challenging, widely used MVTec AD benchmark PatchCore achieves an image-level anomaly detection AUROC score of up to $99.6\%$, more than halving the error compared to the next best competitor. We further report competitive results on two additional datasets and also find competitive results in the few samples regime.\freefootnote{$^*$ Work done during a research internship at Amazon AWS.} Code: github.com/amazon-research/patchcore-inspection.
翻译:能够发现有缺陷的部件是大规模工业制造中的一个关键组成部分。 我们在此工作中处理的一个特殊挑战就是一个寒冷的起始问题:只使用标称(非不合格)示例图像的模型。 虽然每类都有手工制作的解决方案,但目标是建立在很多不同任务上同时运行的系统。 最佳的实用方法是将图像网络模型中的嵌入与外部检测模型结合起来。 本文中, 我们扩展了这项工作线, 并提议 \ textbf{PatchCore}, 它使用一个极具代表性的标称补丁记忆库。 PatchCore 提供了竞争性的引用时间, 同时又在检测和本地化方面实现了最先进的业绩。 在具有挑战性的MVTec AD基准 PatchCore 中, 广泛使用的MVTec Ad基准在图像级异常检测中得分高达99.6 $ 美元, 比下一个最佳匹配者差一半多。 我们进一步报告另外两个数据集的竞争性结果, 并在少数样本系统中找到竞争性的结果。