Visual anomaly detection is essential and commonly used for many tasks in the field of computer vision. Recent anomaly detection datasets mainly focus on industrial automated inspection, medical image analysis and video surveillance. In order to broaden the application and research of anomaly detection in unmanned supermarkets and smart manufacturing, we introduce the supermarket goods anomaly detection (GoodsAD) dataset. It contains 6124 high-resolution images of 484 different appearance goods divided into 6 categories. Each category contains several common different types of anomalies such as deformation, surface damage and opened. Anomalies contain both texture changes and structural changes. It follows the unsupervised setting and only normal (defect-free) images are used for training. Pixel-precise ground truth regions are provided for all anomalies. Moreover, we also conduct a thorough evaluation of current state-of-the-art unsupervised anomaly detection methods. This initial benchmark indicates that some methods which perform well on the industrial anomaly detection dataset (e.g., MVTec AD), show poor performance on our dataset. This is a comprehensive, multi-object dataset for supermarket goods anomaly detection that focuses on real-world applications.

翻译：视觉异常检测在计算机视觉领域的许多任务中至关重要且被广泛应用。近期异常检测数据集主要聚焦于工业自动化检测、医学图像分析和视频监控。为了拓展异常检测在无人超市和智能制造领域的应用与研究，我们引入了超市商品异常检测（GoodsAD）数据集。该数据集包含6124张高分辨率图像，涵盖484种不同外观的商品，分为6个类别。每个类别包含若干常见异常类型，如变形、表面破损和开封。异常包括纹理变化和结构变化。数据集遵循无监督设定，仅使用正常（无缺陷）图像进行训练，并为所有异常提供了像素级精确标注的真实区域。此外，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。这一初始基准表明，部分在工业异常检测数据集（如MVTec AD）上表现优异的方法，在本数据集上性能较差。本数据集是一个面向真实应用场景的综合性、多目标超市商品异常检测数据集。