Robust Uncertainty Estimation for Classification of Maritime Objects
Jonathan Becktor Frederik Scholler Evangelos Boukas Lazaros Nalpantidis
We explore the use of uncertainty estimation in the maritime domain, showing
the efficacy on toy datasets (CIFAR10) and proving it on an in-house dataset,
SHIPS. We present a method joining the intra-class uncertainty achieved using
Monte Carlo Dropout, with recent discoveries in the field of outlier detection,
to gain more holistic uncertainty measures. We explore the relationship between
the introduced uncertainty measures and examine how well they work on CIFAR10
and in a real-life setting. Our work improves the FPR95 by 8% compared to the
current highest-performing work when the models are trained without
out-of-distribution data. We increase the performance by 77% compared to a
vanilla implementation of the Wide ResNet. We release the SHIPS dataset and
show the effectiveness of our method by improving the FPR95 by 44.2% with
respect to the baseline. Our approach is model agnostic, easy to implement, and
often does not require model retraining.