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.

翻译：我们探索了不确定性估计在海洋领域的应用，展示了其在玩具数据集（CIFAR10）上的有效性，并在内部数据集SHIPS上进行了验证。我们提出了一种方法，将利用蒙特卡洛丢弃法实现的类内不确定性与异常检测领域的最新成果相结合，以获取更全面的不确定性度量。我们探究了所引入的不确定性度量之间的关系，并考察了它们在CIFAR10及真实场景下的表现。当模型在未使用分布外数据训练的情况下，我们的工作相比当前性能最佳的方法将FPR95提升了8%。与Wide ResNet的基础实现相比，我们将性能提升了77%。我们发布了SHIPS数据集，并通过将FPR95相对于基准提升44.2%证明了我们方法的有效性。我们的方法具有模型无关性、易于实现，且通常无需重新训练模型。