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.

本研究介绍了超市商品异常检测 (GoodsAD) 数据集，该数据集包含 484 种不同出现方式的商品的 6124 张高分辨率图像，每个类别包含几种常见的异常类型，并对所有异常提供像素精确的地面真实区域，并对当前最先进的无监督异常检测方法进行了彻底的评估。