Out-of-Distribution Object Detection in Street Scenes via Synthetic Outlier Exposure and Transfer Learning

Out-of-distribution (OOD) objects are often silently overlooked by object detectors, which produce no detection for them, leaving downstream modules with nothing to act on and no indication that anything was missed. A reliable object detector must detect OOD objects by localizing and correctly classifying them as OOD. Existing approaches primarily focus on OOD detection for image classification or rely on auxiliary branches, and typically do not treat in-distribution (ID) and OOD objects in a unified manner. We present SynOE-OD, a Synthetic Outlier-Exposure-based Object Detection method, which uses text-guided inpainting and an open-vocabulary object detector to generate and verify synthetic outliers offline. These are then used to fine-tune an object detector of choice with an added OOD class, which requires no auxiliary branch at inference time. On the OoDIS benchmark, SynOE-OD outperforms the strongest open-vocabulary baseline, reaching AP${}_{\mathrm{50..95}}$ of $57.6$ %, $29.4$ % and $39.2$ % on RoadObstacle, RoadAnomaly and Fishyscapes, against $48.5$, $22.7$ and $22.7$. This state-of-the-art performance holds for both fine-tuned closed-set and open-vocabulary detectors while maintaining competitive ID performance in street scenes.