Using Deep Convolutional Neural Network Architectures for Object Classification and Detection within X-ray Baggage Security Imagery
- Submitting institution
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University of Durham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 114333
- Type
- D - Journal article
- DOI
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10.1109/tifs.2018.2812196
- Title of journal
- IEEE Transactions on Information Forensics & Security
- Article number
- -
- First page
- 2203
- Volume
- 13
- Issue
- 9
- ISSN
- 15566013
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2018
- URL
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https://doi.org/10.1109/tifs.2018.2812196
- 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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3
- Research group(s)
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A - Innovative Computing
- Citation count
- 57
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Only openly published performance metric against the UK Home Office X-ray detection evaluation image library. Results replicated and referenced by Liang et al. 2018 (with Smiths Detection https://www.smithsdetection.com/products/icmore/), Liang et al. 2019 (with Rapiscan), Cui et al. 2019, Riffo et al. 2019, Kong et al. 2019 (with Hitachi), Zou et al. / Lui et al. 2019 / Wei et al. 2020, Shih et al., Galvez et al.. Breckon received UK government grants to extend/apply this work to aviation and border security (UK Home Office: BORD-16-240 / BORD-16-252, UK DfT: T-TRIG 2016, FASS ACC101756, ACC500137, ACC107027, ACC6004437, ACC6007893).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -