PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation

TL;DR

This paper introduces GoodsAD, a new high-resolution dataset for unsupervised anomaly detection in supermarket goods, highlighting its diversity, real-world relevance, and providing a benchmark showing current methods' limitations.

Contribution

The paper presents GoodsAD, a comprehensive dataset for supermarket goods anomaly detection with pixel-level annotations, and evaluates existing methods, revealing their limitations in this new domain.

Findings

Current state-of-the-art methods perform poorly on GoodsAD

GoodsAD contains 6124 images of 484 goods with diverse anomalies

Benchmark results highlight the need for improved anomaly detection techniques

Abstract

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.