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 peforming approaches combine embeddings from ImageNet models with an outlier detection model. In this paper, we extend on this line of work and propose 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 standard dataset MVTec AD, PatchCore achieves an image-level anomaly detection AUROC score of $99.1\%$, 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.
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寒冷的起始问题:只使用标称(非缺陷)示例图像来设计一个模型。 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。 最佳的成型方法将图像网络模型的嵌入和外部探测模型结合起来。 在本文中,我们扩展了这一工作线,并提议PatchCore, 它使用极具代表性的标称补丁记忆库。 PatchCore 提供竞争性的引用时间,同时实现最先进的检测和本地化性能。 在标准数据集 MVTec AD 上, PatchCore 实现了一个图像级异常探测分数99.1 $ ⁇, 超过将错误减半到下一个最佳比较器。 我们进一步报告另外两个数据集的竞争结果,并在少数样本系统中找到竞争性结果。