Deep learning for symbols detection and classification in engineering drawings.
- Submitting institution
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Robert Gordon University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- Elyan_3
- Type
- D - Journal article
- DOI
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10.1016/j.neunet.2020.05.025
- Title of journal
- Neural Networks
- Article number
- -
- First page
- 91
- Volume
- 129
- Issue
- -
- ISSN
- 1879-2782
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2020
- URL
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-
- Supplementary information
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- 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
-
-
- Research group(s)
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- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The developed methods enabled our partner (DNV GL LTD - Brian.Bain@dnvgl.com) to significantly reduce processing time from two hours to less than five minutes (https://rgu-repository.worktribe.com/project/123491/data-extraction-from-complex-engineering-drawings) and opened a new collaboration with Canadian Company (Fieri Analytics Ltd, Project ID 965267 - s.sottile.fieri@outlook.com) via a joint collaborative project of £215,000 to create an intelligent framework for processing and analysing mechanical diagrams. It was also featured as one of the successful case studies at the Data Lab Innovation Centre main page (https://www.thedatalab.com/case-studies/dnv-gl-digitisation-of-pid-drawings/)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -