  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.
