Automated detection of fault types in CCTV sewer surveys

Joshua Myrans, Richard Everson, Zoran Kapelan

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)


Sewers must be regularly inspected to prioritise effective maintenance, which can be an expensive and time-consuming process. This paper presents a methodology to automatically identify the type of a detected fault using raw closed circuit television (CCTV) footage. The procedure calculates the GIST descriptor of a video frame containing a fault before applying a collection of random forest classifiers to identify the fault's type. Order oblivious filtering is used to further improve the methodology's performance on continuous footage. The technology, including various classifier architectures, has been validated and demonstrated on CCTV footage collected by Wessex Water. The methodology achieved a peak accuracy of 73% when applied to well-represented fault types, showing promise for future application in the water industry.
Original languageEnglish
Pages (from-to)153-163
Number of pages11
JournalJournal of Hydroinformatics
Issue number1
Publication statusPublished - 1 Jan 2019


  • Automated
  • Fault analysis
  • GIST
  • Random forest
  • Sewers

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