Automated detection of faults in sewers using CCTV image sequences

Joshua Myrans, Richard Everson, Zoran Kapelan

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)

Abstract

Routine CCTV surveys are vital to the effective maintenance of wastewater networks, but their time-consuming nature makes them very expensive. We present a methodology capable of automatically detecting faults within recorded CCTV footage, aiming to improve surveying efficiency. The procedure calculates a feature descriptor for each video frame, before using a machine learning classifier to predict the contents of individual frames. The sequence of predictions is then smoothed using a Hidden Markov Model and order oblivious filtering, incorporating information from the entire sequence of frames. This technique has been demonstrated on footage collected by the Wessex Water, achieving a detection accuracy of over 80% on still images. Furthermore, temporal smoothing on continuous CCTV footage improved false negative rate by more than 20%, to achieve an accuracy of 80%. This last step enables the method to compete with the performance of trained technicians, showing promise for application in industry.

Original languageEnglish
Pages (from-to)64-71
Number of pages8
JournalAutomation in Construction
Volume95
DOIs
Publication statusPublished - 1 Nov 2018
Externally publishedYes

Keywords

  • Fault detection
  • GIST
  • Hidden Markov model
  • Random forest
  • Sewer pipe
  • Support vector machine

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