Automating visual inspection in industrial production lines is essential for
increasing product quality across various industries. Anomaly detection (AD)
methods serve as robust tools for this purpose. However, existing public
datasets primarily consist of images without anomalies, limiting the practical
application of AD methods in production settings. To address this challenge, we
present (1) the Valeo Anomaly Dataset (VAD), a novel real-world industrial
dataset comprising 5000 images, including 2000 instances of challenging real
defects across more than 20 subclasses. Acknowledging that traditional AD
methods struggle with this dataset, we introduce (2) Segmentation-based Anomaly
Detector (SegAD). First, SegAD leverages anomaly maps as well as segmentation
maps to compute local statistics. Next, SegAD uses these statistics and an
optional supervised classifier score as input features for a Boosted Random
Forest (BRF) classifier, yielding the final anomaly score. Our SegAD achieves
state-of-the-art performance on both VAD (+2.1% AUROC) and the VisA dataset
(+0.4% AUROC). The code and the models are publicly available.

自动化视觉检测在工业生产线上对于提高产品质量至关重要。异常检测方法是实现此目的的强大工具。此论文介绍了一种包含具有挑战性真实缺陷实例的新型实际工业数据集，并提出了一种基于分割的异常检测器，其在该数据集上表现出先进性能。