It is well known that computer vision can be unreliable when faced with previously unseen imaging conditions. This paper proposes a method to adapt camera parameters according to a normalizing flow-based out-of-distibution detector. A small-scale study is conducted which shows that adapting camera parameters according to this out-of-distibution detector leads to an average increase of 3 to 4 percentage points in mAP, mAR and F1 performance metrics of a YOLOv4 object detector. As a secondary result, this paper also shows that it is possible to train a normalizing flow model for out-of-distribution detection on the COCO dataset, which is larger and more diverse than most benchmarks for out-of-distibution detectors.

翻译：众所周知，计算机视觉在面对先前未见过的成像条件时可能不可靠。本文提出了一种方法，根据基于归一化流的分布外检测器来调整相机参数。一项小规模研究表明，根据该分布外检测器调整相机参数，可使YOLOv4目标检测器的mAP、mAR和F1性能指标平均提高3到4个百分点。作为次要结果，本文还表明，可以在COCO数据集上训练用于分布外检测的归一化流模型，该数据集比大多数分布外检测器的基准数据集更大且更多样化。