Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection

Chengjie Wang, Wenbing Zhu, Bin-Bin Gao, Zhenye Gan, Jiangning Zhang, Zhihao Gu, Shuguang Qian, Mingang Chen, Lizhuang Ma; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 22883-22892

Industrial anomaly detection (IAD) has garnered significant attention and experienced rapid development. However the recent development of IAD approach has encountered certain difficulties due to dataset limitations. On the one hand most of the state-of-the-art methods have achieved saturation (over 99% in AUROC) on mainstream datasets such as MVTec and the differences of methods cannot be well distinguished leading to a significant gap between public datasets and actual application scenarios. On the other hand the research on various new practical anomaly detection settings is limited by the scale of the dataset posing a risk of overfitting in evaluation results. Therefore we propose a large-scale Real-world and multi-view Industrial Anomaly Detection dataset named Real-IAD which contains 150K high-resolution images of 30 different objects an order of magnitude larger than existing datasets. It has a larger range of defect area and ratio proportions making it more challenging than previous datasets. To make the dataset closer to real application scenarios we adopted a multi-view shooting method and proposed sample-level evaluation metrics. In addition beyond the general unsupervised anomaly detection setting we propose a new setting for Fully Unsupervised Industrial Anomaly Detection (FUIAD) based on the observation that the yield rate in industrial production is usually greater than 60% which has more practical application value. Finally we report the results of popular IAD methods on the Real-IAD dataset providing a highly challenging benchmark to promote the development of the IAD field.