Automated sewer pipeline inspection using computer vision techniques

Saeed Moradi, Tarek Zayed, Farzaneh Golkhoo

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

To facilitate condition assessment in sewer pipeline networks current practice is using the available technologies to visually inspect the internal condition of pipelines. Closed circuit television (CCTV) has been one of the most used methods in North American municipalities in last decades. However, this method requires hours of videos to be inspected by certified inspectors which is time consuming, labor intensive, and error prone. The main objective of this research is to propose an automated approach for inspection and condition assessment of sewer pipelines using computer vision techniques. This research includes two main part: Identifying region of interest (ROI) in sewer inspection videos which are most likely to contain sewer defects, and defect detection and classification among the identified anomalous frames. The ROI detection model employs proportional data modeling using hidden Markov models (HMM) to extract abnormal frames from sewer CCTV videos. In the next step, a deep learning approach using convolutional neural networks (CNN) is proposed to detect the defects and classify them. The presented algorithm has been developed and tested using the data sets from CCTV inspection reports.

Original languageEnglish
Title of host publicationPipelines 2018
Subtitle of host publicationCondition Assessment, Construction, and Rehabilitation - Proceedings of Sessions of the Pipelines 2018 Conference
EditorsJason S. Lueke, Christopher C. Macey
PublisherAmerican Society of Civil Engineers (ASCE)
Pages582-587
Number of pages6
ISBN (Electronic)9780784481653
DOIs
Publication statusPublished - 2018
EventPipelines 2018 Conference: Condition Assessment, Construction, and Rehabilitation - Toronto, Canada
Duration: 15 Jul 201818 Jul 2018

Publication series

NamePipelines 2018: Condition Assessment, Construction, and Rehabilitation - Proceedings of Sessions of the Pipelines 2018 Conference

Conference

ConferencePipelines 2018 Conference: Condition Assessment, Construction, and Rehabilitation
CountryCanada
CityToronto
Period15/07/1818/07/18

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Mechanical Engineering
  • Civil and Structural Engineering
  • Geotechnical Engineering and Engineering Geology

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