Abstract

We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. We learn representations by classifying normal data from the CutPaste, a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Our empirical study on MVTec anomaly detection dataset demonstrates the proposed algorithm is general to be able to detect various types of real-world defects. We bring the improvement upon previous arts by 3.1 AUCs when learning representations from scratch. By transfer learning on pretrained representations on ImageNet, we achieve a new state-of-the art 96.6 AUC. Lastly, we extend the framework to learn and extract representations from patches to allow localizing defective areas without annotations during training。

摘要

我们旨在构造一个在缺陷检测上高表现的模型，这个模型可以检测到未知的图片上的缺陷区域不需要异常数据。最后，我们采用了二步框架去构建异常检测器只使用正常的迅雷数据。我们首先学习自监督的深度表达，然后在学习到的表达上构建一个单分类的生成器。我们通过从裁剪中分类正常的数据来学习表达，一种简单的数据增强方式， 裁剪图片的区域并且在一张大的图片上的任意位置进行粘贴。我们在MVTec异常数据的实例学习上表明这种提出的方法可以被用来去发现不同的真实缺陷。当我们从头开始迅雷时， 我们在原有的方法上提高了3.1AUC。通过在imageNet预训练的特征进行迁移学习，我们使用了全新的96.6的AUC。最后，我们扩展这个框架去学习和提取特征从补丁上， 从而允许在训练中不加标注的定位缺陷区域。

A Framework for Anomaly Detection

In this section, we present our anomaly detection framework for high-resolution image with defects in local regions. Following [54], we adopt a two-stage framework for building an anomaly detector, where in the first stage we learn deep representations from normal data and then construct an one-class classifier using learned representations. Subsequently, in Section 2.1, we present a novel method for learning self-supervised representations by predicting CutPaste augmentation, and extend to learning and extracting representations from local patches in Section 2.4.

异常检测的框架

在这一章节，针对局部区域存在缺陷的高分辨图片， 我们提出了缺陷检测的框架。根据54，我们采用两步框架构造一个异常检测器，第一步我们学习深度特征从一个正常数据中，然后使用学到的表征来构造一个单类型的分类器。随后，在2.1节，我们提出一个新的方法通过预测cutPaste增强来学习自监督的表征，扩展到Section2.4, 从局部patch中学习和提取表征。