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 have upcoming field work planned in the Thunder Bay National Marine Sanctuary, which is a unique site due to its abundance of known and suspected shipwrecks. This work is supported by NOAA Ocean Exploration under Award #NA21OAR0110196.
"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
"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.
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.
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.
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]