PKU-GoodsAD：用于无监督异常检测与分割的超市商品数据集
PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation
AI总结：
提出PKU-GoodsAD超市商品异常检测数据集，含6124张高分辨率图像、484种商品和6个类别，并提供像素级真值与无监督基准评估，揭示现有方法在真实超市场景中的性能不足。
摘要：
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应用广泛。近年来，异常检测数据集主要集中于工业自动化检测、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中的应用与研究，我们提出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种外观不同的商品，分为6个类别。每个类别包含若干常见的不同类型的异常，例如形变、表面损伤和已开封等。异常既包含纹理变化，也包含结构变化。该数据集遵循无监督设定，仅使用正常（无缺陷）图像进行训练。所有异常均提供了像素级精确的真值区域。此外，我们还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，一些在工业异常检测数据集（如MVTec AD）上表现良好的方法，在我们的数据集上表现较差。这是一个面向实际应用的、全面的多目标超市商品异常检测数据集。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
