Optimal Transport Based and Softplus Loss Functions for Anomaly Detection

Anomaly segmentation for urban-driving scenes has gained increasing attention in recent years due to safety concerns in autonomous driving. The ability to accurately detect and segment anomalous objects is critical to ensure the safety of autonomous driving systems in real-world environments. In this paper, we propose two key innovative techniques to address this challenge: (i) introducing a novel inlier training loss derived from the optimal transport, which enhances the ability to identify pixels from inlier classes; and (ii) applying the softplus loss instead of the hinge loss, as the former provides a faster convergence rate and better anomaly segmentation performance. Our approach demonstrates state-of-the-art results, evaluated by the area under precision-recall curve (AuPRC), on the road anomaly and SMIYC (obstacle tracking) datasets, thereby pushing the boundaries of anomaly detection in urban-driving scenarios.