Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development

TL;DR

Texture-AD is a new benchmark dataset for evaluating unsupervised anomaly detection algorithms in industrial settings, featuring real-world defect images with pixel-level annotations across various materials and defect types.

Contribution

The paper introduces Texture-AD, the first dataset dedicated to real-world industrial defect detection, along with a new evaluation method and baseline results.

Findings

Texture-AD is a challenging dataset for current algorithms.

Baseline algorithms perform poorly on Texture-AD.

The dataset covers diverse materials and defect types.

Abstract

Anomaly detection is a crucial process in industrial manufacturing and has made significant advancements recently. However, there is a large variance between the data used in the development and the data collected by the production environment. Therefore, we present the Texture-AD benchmark based on representative texture-based anomaly detection to evaluate the effectiveness of unsupervised anomaly detection algorithms in real-world applications. This dataset includes images of 15 different cloth, 14 semiconductor wafers and 10 metal plates acquired under different optical schemes. In addition, it includes more than 10 different types of defects produced during real manufacturing processes, such as scratches, wrinkles, color variations and point defects, which are often more difficult to detect than existing datasets. All anomalous areas are provided with pixel-level annotations to facilitate comprehensive evaluation using anomaly detection models. Specifically, to adapt to diverse products in automated pipelines, we present a new evaluation method and results of baseline algorithms. The experimental results show that Texture-AD is a difficult challenge for state-of-the-art algorithms. To our knowledge, Texture-AD is the first dataset to be devoted to evaluating industrial defect detection algorithms in the real world. The dataset is available at https://XXX.