  The superior performance of object detectors is often established under the
condition that the test samples are in the same distribution as the training
data. However, in many practical applications, out-of-distribution (OOD)
instances are inevitable and usually lead to uncertainty in the results. In
this paper, we propose a novel, intuitive, and scalable probabilistic object
detection method for OOD detection. Unlike other uncertainty-modeling methods
that either require huge computational costs to infer the weight distributions
or rely on model training through synthetic outlier data, our method is able to
distinguish between in-distribution (ID) data and OOD data via weight parameter
sampling from proposed Gaussian distributions based on pre-trained networks. We
demonstrate that our Bayesian object detector can achieve satisfactory OOD
identification performance by reducing the FPR95 score by up to 8.19% and
increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC
datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD
dataset.
