Final Report: NOAA Ocean Exploration NOFO NA21OAR0110196, Machine Learning for Automated Detection of Shipwreck Sites from Large Area Robotic Surveys

Details

• Personal Author:
  Skinner, Katherine
• Corporate Authors:
  United States. National Oceanic and Atmospheric Administration. Office of Ocean Exploration and Research. ; University of Michigan.
• NOAA Program & Office:
  Ocean Exploration Program ; OAR (Oceanic and Atmospheric Research)
• Description:
  This project developed new technology and innovative methods to advance autonomous capabilities of marine robotic systems for search and survey of shipwreck sites. The main goal of this work was to increase efficiency and decrease cost for these missions. To achieve this goal, we proposed the following objectives: (i) to develop novel machine learning algorithms for online detection and ranking of potential targets of interest from sonar data collected through large area robotic surveys, (ii) to develop novel methods for efficient path planning of robotic surveys to collect high resolution imagery from potential sites of interest, and (iii) to validate proposed methods through large area robotic search and survey of shipwreck sites in the Thunder Bay National Marine Sanctuary (TBNMS) in Lake Huron.
• Keywords:
  Machine Learning Robotics Shipwrecks Underwater Archaeology Underwater Exploration Machine Learning Underwater Archaeology Underwater Exploration FY21 NA21OAR0110196 NOFO

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• DOI:
  https://doi.org/10.25923/85fd-0215
• Format:
  pdf
• Document Type:
  Non-series Report
• Funding:
  Grant no. NA21OAR0110196
• License:
  https://creativecommons.org/publicdomain/zero/1.0/
• Rights Information:
  CC0 Public Domain
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:ecb7e52271016b180c24d5a503dfa859d95fc054426961282a8003bed7fc70d8b58fe582d6c946a9ff54caed817f82b7465901ad8a112f5075e10e832f66c50e
• Download URL:
  https://repository.library.noaa.gov/view/noaa/70899/noaa_70899_DS1.pdf
• File Type:
  [PDF - 7.86 MB ]