OoDDINO:A Multi-level Framework for Anomaly Segmentation on Complex Road Scenes
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
Anomaly segmentation aims to identify Out-of-Distribution (OoD) anomalous
objects within images. Existing pixel-wise methods typically assign anomaly
scores individually and employ a global thresholding 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 introduce OoDDINO, a novel
multi-level anomaly segmentation framework 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 acts 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.
