Research

Current Research

Machine Learning for Automated Detection of Shipwreck Sites from Large Area Robotic Surveys

This project will develop new methods using machine learning to process data collected from underwater robots to automatically detect submerged objects and perform targeted surveys of detected sites. As part of this project, we conducted field work in Lake Huron, MI and produced the AI4Shipwrecks dataset. This work is supported by NOAA Ocean Exploration under Award #NA21OAR0110196.

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STARS: Zero-shot Sim-to-Real Transfer for Segmentation of Shipwrecks in Sonar Imagery

"Building Curious Machines", Michigan Engineering Research News 

Side scan image of the shipwreck Monrovia collected with the Michigan Technological University Great Lakes Research Center IVER-3 AUV. Image courtesy of Machine Learning for Automated Detection of Shipwreck Sites from Large Area Robotic Surveys. 

AI4Shipwrecks Dataset

For this project, we collected sidescan sonar data of various shipwrecks in the Thunder Bay National Marine Sanctuary in Lake Huron, MI. Thunder Bay is a unique site due to its abundance of known and suspected shipwrecks. Field work was conducted during the summers of 2022 and 2023 in collaboration with Michigan Technological University and Louisiana State University. This work is supported by NOAA Ocean Exploration under Award #NA21OAR0110196.

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Project Website

Paper

"Expedition Overview (Year 1)", NOAA Ocean Explorer 

"Scientists travel to the Thunder Bay National Marine Sanctuary", WBKBTV  

"Research team uses robots to search for shipwrecks", The Alpena News 

Photo credits to Darby Hinkley of the Alpena News. From left to right: Onur Bagoren (UM), Anja Sheppard (UM), Mason Pesson (LSU), Corina Barbalata (LSU), William Ard (LSU), Jamey Anderson (MTU), and Katie Skinner (UM).

Past Research

Unsupervised Learning for Processing Underwater Imagery

Deep learning has demonstrated great success in modeling complex nonlinear systems but requires a large amount of training data, which is difficult to compile in subsea environments. Our prior work leverages physics-based models of underwater image formation to develop unsupervised learning approaches to advance perceptual capabilities of underwater robots. In particular, we have focused on unsupervised learning for color correction and depth estimation of monocular and stereo underwater imagery.

UWStereoNet.mp4

Perception for Autonomous Driving

We have also collaborated with the Ford Center for Autonomous Vehicles at University of Michigan to improve perception for autonomous vehicles in urban environments. The videos below show results from our work on transferring sensor-based effects from real data to simulated data to improve results of training on simulated data for the task of object detection. 

CameraEffects_ECCVW2018.mp4

Light Field Imaging in Underwater Environments

Light field cameras have a microlens array between the camera's main lens and image sensor, enabling recovery of a depth map and high resolution image from a single optical sensor. Our research has focused on leveraging light field cameras to improve underwater perception, with tasks including real-time 3D reconstruction and underwater image dehazing.

Underwater Bundle Adjustment

Our work developing underwater bundle adjustment integrates color correction into the structure recovery procedure for multi-view stereo reconstruction in underwater environments. 

Robotic Survey of Sunken Pirate City

Our team conducted a robotic survey of the submerged city of Port Royal, Jamaica to create a 3D reconstruction of the marine archaeological site. [Read more]