Heterogeneous Datasets for Unsupervised Image Anomaly Detection

Juan Lagos, Haider Ali, Adnan Faroque, Esa Rahtu; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 7266-7276

Unsupervised anomaly detection (AD) is a critical task in various domains from manufacturing to infrastructure monitoring. To advance this field we introduce two novel datasets: CARS-AD and ROADS-AD designed to challenge existing unsupervised AD methods with their diverse and heterogeneous image content. CARS-AD comprises real images of cars with various defects while ROADS-AD contains images of roads from multiple countries each presenting unique challenges in anomaly detection. These datasets provide ground truth pixel-wise masks and image-level ground truth labels enabling detailed evaluation and benchmarking of AD algorithms. We evaluate state-of-the-art unsupervised AD methods on both datasets using the AUROC metric to assess detection and localization performance. Our results reveal significant room for improvement underscoring the complexity of the datasets and the need for robust AD techniques. Notably Csflow and U-Flow demonstrate superior performance on the CARS-AD Dataset leveraging their ability to process multi-scale features effectively. Conversely Reverse Distillation excels in anomaly localization on the ROADS-AD Dataset showcasing the importance of nuanced approaches for diverse anomaly types. Our findings underscore the importance of addressing the challenges posed by heterogeneous datasets in unsupervised AD. We hope that the introduction of CARS-AD and ROADS-AD will inspire further research in this field driving the development of innovative AD methods capable of handling real-world anomalies with greater accuracy and reliability. CARS-AD Dataset and ROADS-AD Dataset are publicly available at https://github.com/juanb09111/heterogeneousAD.