OoDDINO: A Multi-level Framework for Anomaly Segmentation on Complex Road Scenes

Anomaly segmentation aims to identify Out-of-Distribution (OoD) anomalous objects within images. Existing pixel-wise methods typi- cally assign anomaly scores individually and employ a global thresh- olding strategy to segment anomalies. Despite their effectiveness, these approaches encounter significant challenges in real-world applications: (1) neglecting spatial correlations among pixels within the same object, resulting in fragmented segmentation; (2) variabil- ity in anomaly score distributions across image regions, causing global thresholds to either generate false positives in background areas or miss segments of anomalous objects. In this work, we intro- duce OoDDINO, a novel multi-level anomaly segmentation frame- work designed to address these limitations through a coarse-to-fine anomaly detection strategy. OoDDINO combines an uncertainty- guided anomaly detection model with a pixel-level segmentation model within a two-stage cascade architecture. Initially, we propose an Orthogonal Uncertainty-Aware Fusion Strategy (OUAFS) that sequentially integrates multiple uncertainty metrics with visual representations, employing orthogonal constraints to strengthen the detection model's capacity for localizing anomalous regions accurately. Subsequently, we develop an Adaptive Dual-Threshold Network (ADT-Net), which dynamically generates region-specific thresholds based on object-level detection outputs and pixel-wise anomaly scores. This approach allows for distinct thresholding strategies within foreground and background areas, achieving fine- grained anomaly segmentation. The proposed framework is compatible with other pixel-wise anomaly detection models, which act as a plug-in to boost the performance. Extensive experiments on two benchmark datasets validate our framework's superiority and compatibility over state-of-the-art methods. Source code is available at: https://github.com/OoDDINO/OoD-DINO.