  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.
