MVTec AD

The detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the field of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new approaches and ideas. We introduce the MVTec Anomaly Detection (MVTec AD) dataset containing 5354 high-resolution color images of different object and texture categories. It contains normal, i.e., defect-free, images intended for training and images with anomalies intended for testing. The anomalies manifest themselves in the form of over 70 different types of defects such as scratches, dents, contaminations, and various structural changes. In addition, we provide pixel-precise ground truth regions for all anomalies. We also conduct a thorough evaluation of current state-of-the-art unsupervised anomaly detection methods based on deep architectures such as convolutional autoencoders, generative adversarial networks, and feature descriptors using pre-trained convolutional neural networks, as well as classical computer vision methods. This initial benchmark indicates that there is considerable room for improvement. To the best of our knowledge, this is the first comprehensive, multi-object, multi-defect dataset for anomaly detection that provides pixel-accurate ground truth regions and focuses on real-world applications

自然图像数据中的异常结构检测，对于计算机视觉领域的诸多任务而言至关重要。无监督异常检测方法的研发，需要可用于训练与评估新方法及新思路的数据集支撑。我们构建了MVTec异常检测（MVTec Anomaly Detection, MVTec AD）数据集，该数据集包含5354幅覆盖不同物体与纹理类别的高分辨率彩色图像。数据集包含用于训练的正常（即无缺陷）图像，以及用于测试的带异常图像。异常以70余种不同类型的缺陷形式体现，例如划痕、凹痕、污染以及各类结构畸变。此外，我们为所有异常样本提供了像素级精确的真值区域（ground truth）标注。我们还针对当前顶尖的无监督异常检测方法开展了全面评估，这些方法涵盖基于卷积自编码器（convolutional autoencoder）、生成对抗网络（generative adversarial network, GAN）以及预训练卷积神经网络特征描述子的深度学习架构，同时也包含传统计算机视觉方法。此次初始基准测试结果表明，现有方法仍存在较大的性能提升空间。据我们所知，本数据集是首个面向真实应用场景、具备像素级精确真值标注的多物体、多缺陷类型综合异常检测数据集。

搜集汇总

背景与挑战

背景概述

MVTec AD是一个工业异常检测基准数据集，包含15类物体和纹理的5000+高分辨率图像，提供无缺陷训练集和带缺陷测试集，并包含像素级异常标注。