A novel weakly-supervised approach for RGB-D-based nuclear waste object detection
- Submitting institution
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University of York
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 66301636
- Type
- D - Journal article
- DOI
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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
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5
- Research group(s)
-
-
- 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. It is the key underpinning research for 'Theme 2: Waste Handling'; of the £42 million National Centre for Nuclear Robotics (NCNR). The paper was cited by Faraday Institution researchers in Nature 575, 2019. The research dataset has been released as a benchmark for nuclear waste-like object recognition.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -