Supervised Anomaly Detection for Complex Industrial Images

Aimira Baitieva, David Hurych, Victor Besnier, Olivier Bernard; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 17754-17762

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.