A Novel Weakly-Supervised Approach for RGB-D-Based Nuclear Waste Object Detection
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 5229
- Type
- D - Journal article
- DOI
-
10.1109/jsen.2018.2888815
- Title of journal
- IEEE Sensors Journal
- Article number
- -
- First page
- 3487
- Volume
- 19
- Issue
- 9
- ISSN
- 1530-437X
- Open access status
- Technical exception
- Month of publication
- December
- Year of publication
- 2018
- 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
-
5
- Research group(s)
-
J - Visual Computing
- Citation count
- 24
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper was one of the core achievements of the Horizon 2020 RoMaNs project for robotic nuclear waste sorting and segregation. The research contributed to the University of Birmingham securing £42 million for the National Centre for Nuclear Robotics (NCNR). It was cited in a Nature review of robotic Lithium battery recycling (doi.org/10.1038/s41586-019-1682-5). The research dataset has been released as a benchmark for nuclear waste-like object recognition (https://sites.google.com/site/romansbirmingham/home).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -