Automated detection of faults in sewers using CCTV image sequences
- Submitting institution
-
University of Exeter
- Unit of assessment
- 12 - Engineering
- Output identifier
- 5111
- Type
- D - Journal article
- DOI
-
10.1016/j.autcon.2018.08.005
- Title of journal
- Automation in Construction
- Article number
- -
- First page
- 64
- Volume
- 95
- Issue
- -
- ISSN
- 0926-5805
- Open access status
- Not compliant
- Month of publication
- November
- 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
-
2
- Research group(s)
-
C - Water and Environment
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This step-change methodology solves the important water industry challenge of automated detection of structural/other faults in sewers from CCTV inspections. Undetected, these faults can lead to major flooding/pollution incidents. Novel methodology reduces the cost and improves reliability of human-based detections. It was developed as part of the EPSRC WISE CDT (EP/L016214/1) using Wessex Water and South West Water data with further validations performed in Finland and Australia. The technology was commercialised via a 2-year Knowledge Transfer Partnership with South West Water (1025165) and led to 5 keynote lectures at international events including the CCWI-WDSA 2020 conference in Beijing.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -