Sidescan sonar images for training automated recognition of submerged body-like objects
This dataset contains sonar image data collected from various locations in Bath and Bristol, UK. It was collected using an autonomous uncrewed surface vessel (USV) equipped with Blueprint Subsea StarFish 450 and StarFish 990 side scanning sonar.
The higher resolution images were used to train convolutional neural networks (CNNs) for autonomous detection of a sunken mannequin, used as a proxy for a drowning victim in missing persons scenarios.
Cite this dataset as:
Rymansaib, Z.,
Nga, Y.,
Anthony Treloar, A.,
Hunter, A.,
2024.
Sidescan sonar images for training automated recognition of submerged body-like objects.
Bath: University of Bath Research Data Archive.
Available from: https://doi.org/10.15125/BATH-01467.
Export
Data
sidescan_sonar … training.zip
application/zip (6GB)
Creative Commons: Attribution 4.0
We request that any users of this dataset please cite the two associated articles: Rymansaib, Z., Thomas, B., Treloar, A. A., Metcalfe, B., Wilson, P., and Hunter, A., 2023. A prototype autonomous robot for underwater crime scene investigation and emergency response. Journal of Field Robotics, 40(5), 983-1002. Available from: https://doi.org/10.1002/rob.22164. Nga, Y. Z., Rymansaib, Z., Anthony Treloar, A., and Hunter, A., 2024. Automated Recognition of Submerged Body-like Objects in Sonar Images Using Convolutional Neural Networks. In: Remote Sensing, 4036. Available from: https://doi.org/10.3390/rs16214036.
Creators
Zuhayr Rymansaib
University of Bath
Yan Nga
University of Bath
Alfie Anthony Treloar
University of Bath
Alan Hunter
University of Bath
Contributors
University of Bath
Rights Holder
Coverage
Collection date(s):
From 5 October 2017 to 8 April 2022
Geographical coverage:
Kennet and Avon Canal, UK. Minerva Bath Rowing Club, Bath, UK. Underfall Yard, Bristol, UK.
Documentation
Data collection method:
Full details of the methodology used to collect the images using side scanning sonar may be found in the paper, "A prototype autonomous robot for underwater crime scene investigation and emergency response".
Methodology link:
Rymansaib, Z., Thomas, B., Treloar, A. A., Metcalfe, B., Wilson, P., and Hunter, A., 2023. A prototype autonomous robot for underwater crime scene investigation and emergency response. Journal of Field Robotics, 40(5), 983-1002. Available from: https://doi.org/10.1002/rob.22164.
Documentation Files
README.txt
text/plain (1kB)
Creative Commons: Attribution 4.0
Funders
Engineering and Physical Sciences Research Council
https://doi.org/10.13039/501100000266
Human-Machine Learning of Ambiguities to Support Safe, Effective, and Legal Decision Making
EP/X030156/1
Publication details
Publication date: 30 October 2024
by: University of Bath
Version: 1
DOI: https://doi.org/10.15125/BATH-01467
URL for this record: https://researchdata.bath.ac.uk/id/eprint/1467
Related papers and books
Nga, Y. Z., Rymansaib, Z., Anthony Treloar, A., and Hunter, A., 2024. Automated Recognition of Submerged Body-like Objects in Sonar Images Using Convolutional Neural Networks. Remote Sensing, 16(21), 4036. Available from: https://doi.org/10.3390/rs16214036.
Rymansaib, Z., Thomas, B., Treloar, A. A., Metcalfe, B., Wilson, P., and Hunter, A., 2023. A prototype autonomous robot for underwater crime scene investigation and emergency response. Journal of Field Robotics, 40(5), 983-1002. Available from: https://doi.org/10.1002/rob.22164.
Contact information
Please contact the Research Data Service in the first instance for all matters concerning this item.
Faculty of Engineering & Design
Mechanical Engineering
Research Centres & Institutes
Advanced Design & Manufacturing @ Bath (ADM)
