Supervised Anomaly Detection for Complex Industrial Images

Aimira Baitieva    David Hurych    Victor Besnier    Olivier Bernard

CVPR 2024 (poster)

Abstract

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

BibTeX

@inproceedings{baitieva2024supervised,
  title={Supervised Anomaly Detection for Complex Industrial Images},
  author={Aimira Baitieva, David Hurych, Victor Besnier and Olivier Bernard},
  booktitle={CVPR},
  year={2024}
}