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 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 annotations for all anomalies. We 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 pretrained convolutional neural networks, as well as classical computer vision methods. We highlight the advantages and disadvantages of multiple performance metrics as well as threshold estimation techniques. This benchmark indicates that methods that leverage descriptors of pretrained networks outperform all other approaches and deep-learning-based generative models show considerable room for improvement.

中文翻译：

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MVTec 异常检测数据集：用于无监督异常检测的综合真实世界数据集

自然图像数据中异常结构的检测对于计算机视觉领域的众多任务至关重要。无监督异常检测方法的开发需要数据来训练和评估新方法和想法。我们介绍了 MVTec 异常检测数据集，其中包含 5354 张不同对象和纹理类别的高分辨率彩色图像。它包含用于训练的正常（即无缺陷）图像和用于测试的异常图像。异常表现为 70 多种不同类型的缺陷，例如划痕、凹痕、污染和各种结构变化。此外，我们为所有异常提供像素精确的地面实况注释。我们对当前最先进的无监督异常检测方法进行了全面评估，这些方法基于深度架构，例如卷积自动编码器、生成对抗网络和使用预训练卷积神经网络的特征描述符，以及经典的计算机视觉方法。我们强调了多个性能指标以及阈值估计技术的优缺点。该基准表明，利用预训练网络描述符的方法优于所有其他方法，基于深度学习的生成模型显示出相当大的改进空间。我们强调了多个性能指标以及阈值估计技术的优缺点。该基准表明，利用预训练网络描述符的方法优于所有其他方法，基于深度学习的生成模型显示出相当大的改进空间。我们强调了多个性能指标以及阈值估计技术的优缺点。该基准表明，利用预训练网络描述符的方法优于所有其他方法，基于深度学习的生成模型显示出相当大的改进空间。