Beyond Pixel Uncertainty: Bounding the OoD Objects in Road Scenes

Recognizing out-of-distribution (OoD) objects on roads is crucial for safe driving. Most existing methods rely on segmentation models' uncertainty as anomaly scores, often resulting in false positives -especially at ambiguous regions like boundaries, where segmentation models inherently exhibit high uncertainty. Additionally, it is challenging to define a suitable threshold to generate anomaly masks, especially with the inconsistencies in predictions across consecutive frames. We propose DetSeg, a novel paradigm that helps incorporate object-level understanding. DetSeg first detects all objects in the open world and then suppresses in-distribution (ID) bounding boxes, leaving only OoD proposals. These proposals can either help previous methods eliminate false positives (DetSeg-R), or generate binary anomaly masks without complex threshold search when combined with a box-prompted segmentation module (DetSeg-S). Additionally, we introduce vanishing point guided Hungarian matching (VPHM) to smooth the prediction results within a video clip, mitigating abrupt variations of predictions between consecutive frames. Comprehensive experiments on various benchmarks demonstrate that DetSeg significantly improves performance, reducing the FPR 95 of previous methods by up to 37.45%, offering a more robust and practical solution. Code: https:// github.com/huachao0124/DetSeg-official.