Automatic annotation of subsea pipelines using deep learning
- Submitting institution
-
University of Strathclyde
- Unit of assessment
- 12 - Engineering
- Output identifier
- 111817241
- Type
- D - Journal article
- DOI
-
10.3390/s20030674
- Title of journal
- Sensors
- Article number
- 674
- First page
- -
- Volume
- 20
- Issue
- 3
- ISSN
- 1424-8220
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
13
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The outputs of the paper were key to securing £243k funding from the Oil and Gas Innovation Centre (OGIC Grant No.18PR-16) to extend the collaboration with international industry partner N-Sea (contact: Hein Filius).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -