Zhiyong Zhang

An Iterated Labeling Method for Annotating Imagery for Fisheries Stock Assessment

Zhiyong Zhang, Samson Braun, Pushyami Kaveti

We present a methodology for fisheries-related data that allows us to converge on a labeled image dataset by iterating over the dataset with multiple training and production loops that can exploit crowdsourcing interfaces. We present our algorithm and its results on two separate sets of image data collected using the Seabed autonomous underwater vehicle. The first dataset comprises of 2,026 completely unlabeled images, while the second consists of 21,968 images that were point annotated by experts. Our results indicate that training with a small subset and iterating on that to build a larger set of labeled data allows us to converge to a fully annotated dataset with a small number of iterations. Even in the case of a dataset labeled by experts, a single iteration of the methodology improves the labels by discovering additional complicated examples of labels associated with fish that overlap, are very small, or obscured by the contrast limitations associated with underwater imagery.

Towards A COLREGs Compliant Autonomous Surface Vessel

Zhiyong Zhang, James Connor Meyers, Thomas Sayre McChord, Hanumant Singh

In this paper, we look at the role of autonomous navigation in the maritime domain. Specifically, we look at how an Autonomous Surface Vessel (ASV) can achieve obstacle avoidance based on the Convention on the International Regulations for Preventing Collisions at Sea (1972), or COLREGs, in real-world environments. To mimic conditions found in the marine vessel environment, we examine the case where the ASV is equipped with a broadband marine radar, an Inertial Navigation System (INS), and uses official Electronic Navigational Charts (ENC).