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In this paper we explore the problem of open set object recognition in maritime domains.
In recent years, deep neural-networks have gained popularity in ship detection and classification problems, but there is little related work in applying Out-of-Distribution (OOD) methods in the maritime domain, or handling vessel classes that fall outside the training data lexicon. One major issue is the open set recognition problem of detecting unknown ships or vessels in the wild. If little or no training data exists for a rare vessel class, what level of performance can we expect from a network when it encounters these objects at inference time? In related object-classification work, deep neural networks have been shown to incorrectly classify OOD samples with high confidence. We apply OOD detection methods to synthetic overhead imagery and a deep maritime-vessel classifier to benchmark performance during rare vessel encounters, and understand how to gracefully resolve these events.
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