Augmenting Anomaly Detection Datasets with Relighting and Depth Estimation

Variations in lighting from weather, day–night cycles, seasonal changes, or artificial sources can cause data drift in deep learning models by altering scene appearance, which leads to degraded performance. This is particularly problematic for outdoor anomaly detection, where training and testing datasets are usually limited to daytime recordings. In this paper, we present initial research on using an image relighting model with depth estimation to build augmented surrogate models of datasets under diverse lighting conditions. We show that such datasets pose significant challenges for anomaly detectors and can enable the development of more robust models. Furthermore, these surrogate relit datasets can support a wide range of outdoor vision tasks.