Automatic Identification of Sewer Fault Types Using CCTV Footage

8 pages•Published: September 20, 2018

Joshua Myrans, Zoran Kapelan and Richard Everson

Abstract

Water companies all over the world regularly perform inspections of their sewer networks. The data collected this way is then analysed by human technician which is time consuming and expensive. Previous work by the authors has developed methodology that can automatically detect faults in sewer pipes using standard CCTV footage. This paper presents a methodology to automatically identify types of detected faults aiming to further improve the efficiency and accuracy (i.e. consistency) of surveys. The methodology calculates a feature descriptor for individual frames of CCTV footage, before predicting the contents using a multi-class Random Forest classifier. Demonstrated on a comprehensive library of frames extracted from real-life CCTV footage of a UK water company, the methodology correctly identified the fault type in 71% of frames. Most common fault types were included in this experiment, covering a wide range of pipe sizes and materials, including vitrified clay, PVC and brick. Overall, this preliminary work shows promise for application in industry, proving an effective tool for analysing CCTV surveys.

Keyphrases: automatic, fault detection, machine learning, random forest, sewers

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 1478-1485.

Links:	https://easychair.org/publications/paper/ZQH3
	https://doi.org/10.29007/w41w

BibTeX entry

@inproceedings{HIC2018:Automatic_Identification_Sewer_Fault,
  author    = {Joshua Myrans and Zoran Kapelan and Richard Everson},
  title     = {Automatic Identification of Sewer Fault Types Using CCTV Footage},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/ZQH3},
  doi       = {10.29007/w41w},
  pages     = {1478-1485},
  year      = {2018}}

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