OoDDINO:A Multi-level Framework for Anomaly Segmentation on Complex Road Scenes
Existing pixel-level anomaly segmentation methods suffer from two key limitations: (1) neglecting pixel-wise spatial correlations, leading to fragmented segmentations, and (2) employing global thresholds ill-suited to foreground/background heterogeneity, causing false positives and missed detections. To address these challenges in complex road scenes, we propose a coarse-to-fine multi-stage detection framework. First, object-level localization mitigates fragmentation; second, an orthogonal uncertainty-aware fusion strategy enhances robustness in anomaly localization. Furthermore, we introduce an adaptive dual-threshold network that enables discriminative pixel-wise segmentation for foreground and background regions. Our framework adopts an uncertainty-guided cascaded architecture, supporting plug-and-play integration of diverse feature representations and uncertainty estimators. Evaluated on two benchmark datasets, our method achieves significant improvements over state-of-the-art approaches, demonstrating superior accuracy, fine-grained segmentation capability, and model compatibility.