Heterogeneous Datasets for Unsupervised Image Anomaly Detection

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 unsu-pervised 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 capa-ble 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.