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.
@article{arxiv.2305.09293,
title = {Out-of-Distribution Detection for Adaptive Computer Vision},
author = {Simon Kristoffersson Lind and Rudolph Triebel and Luigi Nardi and Volker Krueger},
journal= {arXiv preprint arXiv:2305.09293},
year = {2023}
}
Comments
Published in Springer Lecture Notes for Computer Science Vol. 13886 as part of the conference proceedings for Scandinavian Conference on Image Analysis 2023