5 – An Approach to Rapidly Curate Large Image Datasets to Train Machine Learning Ship Classification Models

A critical bottleneck in machine learning efforts continues to be either the lack of sufficiently sized, fully curated data-sets, or availability of the time and resources required to develop the required data-sets/models via manual identification and tagging. Because large curated data-sets are essential to ship identification and classification using machine learning, Rite-Solutions proposes an approach using weakly supervised learning to automatically generate labels for non-curated data-sets to train ship recognition and classification ML models.Several tools exist on the market and in academia that have a range of capabilities that, when integrated, will provide Weak Supervision, e.g., an automated way of curating data-sets. Weakly supervised machine learning shows strong potential to accurately perform the label and training functions through automation and thereby reduce SME effort and time to develop the models required for high confidence threat/non-threat vessel identification. Equally important, this approach will rapidly incorporate new images and data to aid the warfighter in identifying and classifying new and changing threats.