Out-of-Distribution Detection for Adaptive Computer Vision
Simon Kristoffersson Lind Rudolph Triebel Luigi Nardi Volker Krueger
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.