13 citations · 22 across the 6 of their papers we have counts for
6 papers
mbariml: a curation pipeline for turning deep-sea imagery and video into object-detection training data
Lonny Lundsten, Kevin Barnard, Dave Caress
Training data quantity and quality greatly affect object detection model performance, regardless of model architecture. For object detection in deep-sea video and imagery, where th…
DeepSea MOT: A benchmark dataset for multi-object tracking on deep-sea video
Kevin Barnard, Elaine Liu, Kristine Walz +3
Benchmarking multi-object tracking and object detection model performance is an essential step in machine learning model development, as it allows researchers to evaluate model det…
FathomVerse: A community science dataset for ocean animal discovery
Genevieve Patterson, Joost Daniels, Benjamin Woodward +4
Can computer vision help us explore the ocean? The ultimate challenge for computer vision is to recognize any visual phenomena, more than only the objects and animals humans encoun…
The FathomNet2023 Competition Dataset
Eric Orenstein, Kevin Barnard, Lonny Lundsten +3
Ocean scientists have been collecting visual data to study marine organisms for decades. These images and videos are extremely valuable both for basic science and environmental mon…
FathomNet: A global image database for enabling artificial intelligence in the ocean
Kakani Katija, Eric Orenstein, Brian Schlining +8
The ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. As…
FathomNet: An underwater image training database for ocean exploration and discovery
Océane Boulais, Ben Woodward, Brian Schlining +4
Thousands of hours of marine video data are collected annually from remotely operated vehicles (ROVs) and other underwater assets. However, current manual methods of analysis imped…