Transfer Learning of Deep Neural Networks for Visual Collaborative Maritime Asset Identification

Recent advances in deep learning for visual recognition demonstrate high performing pipeline for building and deploying well-performing content models. These advances come with underlying assumptions of the data characteristics pertaining to consumer image and video and availability of the large set of annotated data. In this paper we show how to apply lessons learned in the consumer domain to overhead maritime video corpora. We present how to successfully tune deep learning network to overhead maritime domain and tune parameters to new domain characteristics to achieve high performance metric with smaller set of domain annotations. This approach improves the state-of-the-art metric by 80% on maritime IPATCH data. Next, we present challenges and propose several approaches on user collaboration for maritime asset identification, and introduce the notion of persistent and intermittent models.