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In this paper, we present a methodology for fisheries-related data thatallows us to converge on a labeled image dataset by iterating over the datasetwith multiple training and production loops that can exploit crowdsourcinginterfaces. We present our algorithm and its results on two separate sets ofimage data collected using the Seabed autonomous underwater vehicle. The firstdataset comprises of 2,026 completely unlabeled images, while the secondconsists of 21,968 images that were point annotated by experts. Our resultsindicate that training with a small subset and iterating on that to build alarger set of labeled data allows us to converge to a fully annotated datasetwith a small number of iterations. Even in the case of a dataset labeled byexperts, a single iteration of the methodology improves the labels bydiscovering additional complicated examples of labels associated with fish thatoverlap, are very small, or obscured by the contrast limitations associatedwith underwater imagery.

An Iterative Labeling Method for Annotating Fisheries Imagery